Staphylococcus aureus is an important model organism and pathogen. This S. aureus proteome overview details shared and specific proteins and selected virulence-relevant protein complexes from representative strains of all three major clades. To determine the strain distribution and major clades we used a refined strain comparison combining ribosomal RNA, MLST markers, and looking at highly-conserved regions shared between strains. This analysis shows three sub-clades (A–C) for S. aureus. As calculations are complex and strain annotation is quite time consuming we compare here key representatives of each clade with each other: model strains COL, USA300, Newman, and HG001 (clade A), model strain N315 and Mu50 (clade B) and ED133 and MRSA252 (clade C). We look at these individual proteomes and compare them to a background of 64 S. aureus strains. There are overall 13,284 S. aureus proteins not part of the core proteome which are involved in different strain-specific or more general complexes requiring detailed annotation and new experimental data to be accurately delineated. By comparison of the eight representative strains, we identify strain-specific proteins  that characterize each strain and analyze pathogenicity islands if they contain such strain-specific proteins. We identify strain-specific protein repertoires involved in virulence, in cell wall metabolism, and phosphorylation. Finally we compare and analyze protein complexes conserved and well-characterized among S. aureus , as well as predict and analyze several individual protein complexes, including structure modeling in the three clades. E. coli , and th[E. coli  and in o[E. coli ). HoweveS. aureus [S. aureus is a Gram-positive model organism and a challenging pathogen in clinical infections. It is not easy to establish a general overview on the S. aureus proteome and protein complexes: identification of conserved and strain-specific proteins requires all-against-all sequence comparisons; structure predictions require detailed calculations even for a single protein complex. Nevertheless, in order to have a good strain overview and look at representative proteins and protein complexes we first performed a refined strain comparison combining two well-established phylogenetic markers, i.e., ribosomal RNA and MLST markers . For the best phylogenetic resolution we then considered highly-conserved regions shared between S. aureus strains. Based on this high-resolution analysis and considering the 64 S. aureus genomes completely known we can show that there are three sub-clades (A-C) encompassing all S. aureus strains and give a first view on the complete repertoire of proteins and complexes conserved among all these strains. In order to avoid both too complex calculations, and the annotation of all strains individually and completely for each protein, we next compare key representatives of each clade amongst each other: model strains COL, USA300, Newman, and HG001 (clade A), model strain N315 and Mu50 (clade B), and ED133 and MRSA252 (clade C). We establish strain-specific proteins that distinguish the different strains from each other and look at pathogenicity islands with a high number of strain-specific proteins. Next, we analyze important protein repertoires involved in virulence, cell wall component/glycosylation and look at individual strain-specific protein complexes in the three clades. For strain-specific protein complexes we give several detailed structure predictions. Furthermore, the sequence comparisons are complemented by predictions from bioinformatics using three different gene context methods, evidence from databases, co-expression, and text mining. We also indicate which of these interactions are of particular interest for further experimental investigation.Omics studies, in particular proteomics, are essential in understanding and revealing the life style of . aureus ,6. S. auS. aureus strains. This highlights the need for detailed systems biological investigations and high-throughput experiments to better understand the suggested interactions and complexes as well as their intricate regulation. Several of these improve S. aureus adaptation and its challenging capacity for infection. As a first overview, our study shows which proteins and complexes are conserved among all three S. aureus clades and models strain-specific proteins and protein complexes from key representatives of each clade.We find that there is surprisingly high diversity, complexity and adaptation potential of proteome and protein complexes amongst in silico by sufficient identity of amino acids (>50%) and respective coverage (the shorter partner covered 75% of the partner protein sequence and up to 125% for the longer partner). The reasoning here was that these high criteria for sequence identity and sequence coverage identify, in most cases, true orthologs and, in particular, functional identical proteins in the two compared strains. In addition, local synteny was considered to determine all the S. aureus core genes. Non-coding genes (in particular RNA genes) were carefully excluded from this comparison as the proteome was analyzed.A systematic genome comparison included 64 genomes  and applS. aureus strains is high . However, the total number of known, well-annotated, and conserved protein complexes over all compared S. aureus strains is lower  database was updated to the most recent version of SCOP 1.75 release) . Further5 releaseS. aureus. This is a huge task; hence, we were only able to focus on well-characterized strains representative of the three phylogenetic clades of S. aureus. We, thus, give only an overview on the protein inventory of these strains and point out specific protein complexes. Pictures focus on those few proteins whose structure information and modeling templates are available. The different clades are only visible if, first, a phylogenetic analysis is done. Phylogenetic analysis was performed on the genome data of 64 completely-sequenced S. aureus strains. MLST markers  and highly-conserved regions shared between strains are reported here for the first time, as well as 16S rRNA genes that were selected for global alignment. The generated profile was further analyzed in PhyML [S. aureus COL, USA300, Newman, HG001, N315, Mu50, ED133, and MRSA252 . For the best phylogenetic resolution we took highly-conserved regions into account that are shared between most S. aureus strains as well as concatenated established maker genes  and the information from 16S ribosomal RNA. Taken together, this offers us an improved view on their phylogenetic relationship and leads to the phylogenetic tree shown in To have a good overview on all S. aureus strains compared become readily apparent from this phylogenetic view. Note S. aureus MSHR1132 (Genbank accession number: FR821777) was excluded from this comparison, because it was recently reassigned to the new species Staphylococcusargenteus [S. aureus . Interestingly, it turns out that several often-used strains in the A clade are clonal [Three major clades formed from the 64 rgenteus . Figure e clades . We see e clades : most ofe clonal . To acquS. aureus proteome and to identify functional, as well as physical interactions that take place between proteins thereof, we compared five strains—COL, HG001, Newman, and USA300 (clade A)—to N315 (clade B), regarding their proteome  from the three major clades against the background of all strains in a full Venn diagram. To gain further insight into the proteome  as predi genomes . For comS. aureus strains helps to define the central core proteome. Furthermore, the strain-specific proteins in the model strains were determined. A strain-specific protein list from a complete all-against-all comparison of protein coding genes is, instead, less informative, as it increasingly minimizes strain-specific genes as more genomes are compared. The strain-specific complexes can be predicted with near certainty if we consider enzyme subunits, ABC transporters, and gene context methods, as well as a protein interaction database . For leE. coli, yeast, human), and a huge number of gene expression datasets as co-expression of two proteins is another predictor that these two proteins interact. Finally, evidence from literature is considered; in particular, if two proteins are mentioned together in research articles. In addition we looked at protein homologies and information from proteins in related organisms. All of these indications for protein-protein interactions are integrated into a total Bayesian score for the probability that two proteins interact. We considered only highly reliable interaction predictions  of clade-representative strains against the background of 64 S. aureus strains chosen as representatives for the three clades included again for clade A: COL (Genbank accession number: NC_002951), N315 (Genbank accession number: NC_002745), HG001 (Genbank accession number: NC_007795), and USA300_TCH1516 (Genbank accession number: NC_010079). For clade B Newman (Genbank accession number: NC_009641) and Mu50 (Genbank accession number: NC_002758) were chosen, and for clade C we looked at ED133 (Genbank accession number: NC_017337) and MRSA252 (Genbank accession number: NC_002952). S. aureus strains. Detailed data are found in S. aureus strains compared (see discussion). Individual S. aureus strains were also directly analyzed for their content of metabolic enzymes. Variation is, again, not too high: the number of annotated metabolic enzymes in S. aureus strain COL is 1145, in Mu50 it is 1181, and in ED133 it is 1160. Supplementary Material gives further detailed data, details are found on conserved proteins and protein complexes , yent1 (SA1645), seg (SA1642), sen (SA1643), and seo (SA1648) could form a complex in S. aureus N315 . However, as a first observation supporting that the two Yent proteins really form a complex, these two proteins only occur together in S. aureus strains, they are SAPI-encoded and if they are absent, they are both absent. Regarding the other proteins Seg (SA1642), Sen (SA1643), and Seo (SA1648), there is some evidence for interaction as suggested by gene neighborhood and homology. Furthermore, the proteins Sen and Seo have also co-expression evidence for interaction. Hence we predict direct physical interaction for proteins Yent1 and Yent2, but only weaker  association for the other three. However, we can probably be even more confident about the complex of the two as Yent1 and Yent2 function only together to yield the functional enterotoxin, otherwise they behave as non-functional pseudogenes [Enterotoxin genes eus N315 . We predudogenes .In other strains the virulence-associated genes form often functional associations. This is sketched for a protein complex in N315 , composed of a beta-lactam sensor and mecA-type methicillin resistance repressor, MecI. That both are transferred together with several other genes involved in methicillin resistance and reside in the bacterial membrane together is necessary for a good response against methicillin ,25.S. aureus [Moreover, there is an ABC transporter complex ; the first one is subunit A, the second one, subunit BC. The ABC transporter complex is well-established. Finally, there is a large toxin gene cluster in N315 . The genes are direct neighbors, reside in the same location in the chromosome, and are co-expressed. So, at least functionally, they work together; they are also probably found together on the membrane. There is another strain-specific enterotoxin protein in Newman. These enterotoxins are also considered as vaccine targets against . aureus .As a further clade B member, the strain NCTC8325 and its derivative HG001 are endowed with a lot of phage-specific genes and specific transporter units—all these proteins can also be termed parts of their corresponding complex, but these are at present only predictions. To compare clade B-specific protein complexes, we looked at N315 and Mu50 showing strain-specific variation in methicillin resistance repressor MecI protein, a part of the protein complex involved in methicillin resistance .S. aureus is an important model organism and pathogen. Its proteome is well studied [S. aureus strains representing the three clades of S. aureus strains. We started from three extensive studies in systems biology to delineate a set of conserved core complexes in S. aureus [ studied ,28, howe. aureus ,4,8. As S. aureus strains share particularly well their central metabolism, a recurrent theme in bacteria [S. aureus COL. The structure annotation approach used [S. aureus proteins. However, calculating homology models from such data, the S. aureus clades as pictures and views on the three dimensional structure of selected S. aureus proteins from different protein complexes.bacteria . Enzymesach used  gives thFurthermore, looking at the strictly strain-specific protein lists calculated from extensive sequence comparisons instead  shows thS. aureus complexes is challenging as there is also variation in S. aureus protein complexes over time and there are different modes to regulate this  is deleted [One regulatory mechanism involved in the flexibility of protein complexes is post-translational modifications. Modifications, such as protein phosphorylation, glycosylation, and acetylation represent an efficient means to regulate the activity of the individual subunits and, thus, the entire ensemble of proteins as such. As modifying moieties could be rapidly removed or added, protein functionality and/or structure quickly becomes adapted to environmental changes, such as the transition from aerobic to anaerobic conditions. From our data it becomes clear that protein modifications might indeed play a fundamental role in the regulation of protein complexes and their assembly. For the Ser/Thr kinases, for example, we observe that, in contrast to all other strains, the  deleted .S. aureus variants.The phylogenetic analysis points out that the three USA300 strains are assigned to the same clade, but are quite distinct from each other, in particular the first characterized USA300 isolate FPR3757 , which iS. aureus and their changes is a direct route to identify important switches involved in systems biological adaptation. With these data, more detailed investigations of these S. aureus COL protein complexes and protein structures are possible; for instance, detailed investigations on the whole complex, its assembly and disassembly , or direct targeting of such protein complexes by different drugs, which then requires a systems biological analysis of these drug effects .S. aureus protein complexes is both interesting and challenging, and why a general overview on the protein complex repertoire available for S. aureus strains is important though it can only focus on selected, but representative examples.Regarding clinical relevant isolates such as the USA300 variants examined, our results point out promising targets for direct pharmacological intervention. For instance, to prevent protein complex formation, there is now a range of novel peptide-based or chemically-improved inhibitors available . This cS. aureus strains. We look at all three major clades and comparing against a background of 64 strains where full genome information is available. Strain-specific proteins often allow for specific virulence factors and cell wall synthesis. Several such protein structures were examined in detail, annotating domains with known three-dimensional structure and giving selective examples for the full protein repertoire available for S. aureus strains established by the extensive all-against-all sequence comparisons. Though we established reliable data combining bioinformatics with data available from literature and databases, much more research is required to completely understand the details of protein complexes and their flexible adaptation in S. aureus.Protein complexes form a backbone of adaptation. We show the conserved, as well as the strain-specific, protein complexes for eight representative


To study virus–host protein interactions, knowledge about viral and host protein architectures and repertoires, their particular evolutionary mechanisms, and information on relevant sources of biological data is essential. The purpose of this review article is to provide a thorough overview about these aspects. Protein domains are basic units defining protein interactions, and the uniqueness of viral domain repertoires, their mode of evolution, and their roles during viral infection make viruses interesting models of study. Mutations at protein interfaces can reduce or increase their binding affinities by changing protein electrostatics and structural properties. During the course of a viral infection, both pathogen and cellular proteins are constantly competing for binding partners. Endogenous interfaces mediating intraspecific interactions—viral–viral or host–host interactions—are constantly targeted and inhibited by exogenous interfaces mediating viral–host interactions. From a biomedical perspective, blocking such interactions is the main mechanism underlying antiviral therapies. Some proteins are able to bind multiple partners, and their modes of interaction define how fast these “hub proteins” evolve. “Party hubs” have multiple interfaces; they establish simultaneous/stable (domain–domain) interactions, and tend to evolve slowly. On the other hand, “date hubs” have few interfaces; they establish transient/weak (domain–motif) interactions by means of short linear peptides (15 or fewer residues), and can evolve faster. Viral infections are mediated by several protein–protein interactions (PPIs), which can be represented as networks , with proteins being depicted as nodes, and their interactions as edges. It has been suggested that viral proteins tend to establish interactions with more central and highly connected host proteins. In an evolutionary arms race, viral and host proteins are constantly changing their interface residues, either to evade or to optimize their binding capabilities. Apart from gaining and losing interactions via rewiring mechanisms, virus–host PINs also evolve via gene duplication ; conservation (orthology); horizontal gene transfer (HGT) (xenology); and molecular mimicry (convergence). The last sections of this review focus on PPI experimental approaches and their limitations, and provide an overview of sources of biomolecular data for studying virus–host protein interactions. Compared to the relatively well-conserved processes found in cellular organisms, viruses demonstrate huge variations in terms of genomic composition, patterns of evolution, and protein function. While studying protein–protein interactions (PPIs) in virus–host systems, these variations on the pathogen side must be considered. A large proportion of the PPIs are mediated by domain–domain interactions (DDIs), and viruses belonging to different Baltimore groups have specific domain repertoires, providing different strategies and mechanisms of molecular recognition to accomplish their replication cycle  and affinity-purification mass spectrometry (AP-MS), which present specific advantages and disadvantages . This drug binds the cellular co-receptor CCR5, preventing it from interacting with GP120  and centrality , these domains are likely to be real interacting partners in both systems (interologs). Although the mere presence of orthologs in two systems does not necessarily imply a direct interaction among them , and takes place in the extracellular region (GO:0005576). The viral cyclin (vCyclin) was probably acquired by HGT, and is capable of modulating cellular growth (GO:0005125) in similar ways to cellular cyclins D  is another usual mechanism of protein network evolution, and duplicated genes are common in some viral genomes. After a duplication event, each paralog can undergo independent mutations, giving rise to new biological functions  that mimics this interaction with Ephrin receptors using an interface similar to the one explored by Ephrin type-A receptors 4  are tagged with a recombinant fusion tag, which is then used to purify baits and their respective interacting partners (preys). Once purified, each component in the protein complex can be determined by mass spectrometry  have increasingly gaining popularity. FRET-Based techniques allow the expression of each respective target protein in their native environment, as a single molecule fused with a fluorescent donor/acceptor. Such methods rely on a physical phenomenon of energy transferring between two fluorophores : one protein receives light at a specific wavelength and transfers the energy to its interactor, which in turn emits light of other wavelength/color, which is captured by sensors  information , VirHostNet (PPIs), and PDB (Structures) it is possible to assess the level of data availability for several viral families. As shown in Figure Viruses are pathogens with rather compact genomes that nevertheless provide them with versatile molecular tools able to cause extensive changes in cellular processes. Such versatility can be credited to domain repertoires encoded by viruses, as well as to their mechanisms of molecular evolution. The aspects addressed in this review provide starting points not just to virologists willing to explore integrative approaches to understand viral evolution, but also to computational biologists wanting to understand more about the peculiarities of viral biology in order to develop predictive models for virus–host PPIs.Advances in biomolecular research over the last decades now allow us to tackle important questions regarding virus–host interactions by integrating data from multiple levels of biological complexity. As shown in Figure AB and JP wrote the manuscript. AB collected the data and created the figures. All authors read and approved the final manuscript.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.


Internally symmetric proteins are proteins that have a symmetrical structure in their monomeric single-chain form. Around 10–15% of the protein domains can be regarded as having some sort of internal symmetry. In this regard, we previously published SymD (symmetry detection), an algorithm that determines whether a given protein structure has internal symmetry by attempting to align the protein to its own copy after the copy is circularly permuted by all possible numbers of residues. SymD has proven to be a useful algorithm to detect symmetry. In this paper, we present a new parallelized algorithm called Parallel-SymD for detecting symmetry of proteins on clusters of computers. The achieved speedup of the new Parallel-SymD algorithm scales well with the number of computing processors. Scaling is better for proteins with a larger number of residues. For a protein of 509 residues, a speedup of 63 was achieved on a parallel system with 100 processors. Not only multimeric proteins and protein complexes, but also the repeating units in monomeric proteins are arranged in a symmetric manner. We previously reported a method called SymD  and a weIn comparison to other existing algorithms, SymD has proven to be a robust algorithm to determine internal symmetry in proteins. On the other hand, the exponential increase in computer power has made it possible to perform complex matrix operations in much less time. Many techniques exist for detecting the internal symmetry in protein domains. Some techniques use the structure alignment program –8 and otThe SymD algorithm makes use of an alignment scan procedure where the original protein structure is aligned to copies of itself obtained by circular permutation of all possible numbers of residues. A webserver of SymD algorithm has also been made publicly available . SymD haThe large size of protein structures presents a challenge to commonly used symmetry detection algorithms. The time taken to compute the symmetry of protein structure increases significantly with the size of protein structure due to the huge number of matrix operations. In spite of being consistently accurate across all protein domains, SymD lags behind in terms of speed and scalability. With little increase in input size, the speed decreases drastically, and, moreover, it does not have the capability to scale over multiple processors and multiple cores.To reduce the limitation of SymD, we have come up with a new parallel algorithm that basically uses the same SymD design at its core but extends its capability to multiple processors and distributed computing systems which in turn help SymD to utilize multiple processors available to improve the speed of computation.The need of parallelization arises because of this increased computational time with respect to the size of protein structures. Conversely, contemporary computers have typically multiple computing units (cores) . AlthougThe message passing interface (MPI) standard for communication in parallel computing offers a solution to this problem . The cenIn this article, we present Parallel-SymD, the parallelized SymD algorithm for detection of internal symmetry in protein domains. This new algorithm that detects symmetry in protein domains is especially suited to efficient execution on multiple CPUs and computer of varying power interconnected in a network, which are available in contemporary computing platforms or computing clouds. When detecting the symmetry of a protein that has 509 residues, Parallel-SymD is around 63 times faster compared to the existing SymD algorithm when run in a parallel system using 100 processors. We explain the SymD algorithm, provide the description of our new Parallel-SymD algorithm, and discuss the performance and comparison between the two algorithms.We describe Parallel-SymD algorithm by describing in short the original SymD algorithm  followedN − 3, where N is the number of residues of the protein. The algorithm then finds the best non-self-structural alignment between the original and each of the N − 3 permuted structures. This process is called the “alignment scan.” The best alignment with each permuted structure is obtained using the RSE algorithm [Z-score [T-score, which is a weighted number of aligned residues, similar to the sum of the similarity matrices Sij of Gerstein and Levitt [The SymD  algorithlgorithm , which ilgorithm , 20 is ulgorithm . The two[Z-score  of the Td Levitt , 23, is N − 3 alignment scores and also the position and orientation of the symmetry axis for each of the alignments. The information about the position and orientation of the symmetry axis is obtained from the transformation matrix. Finally, a protein is deemed to be symmetric if one of the Z-scores of the alignments is greater than the cut-off value (Z-score of 8 or 10). It has to be noted here that symmetricity is not exact and it depends on the scoring function and the cut-off value associated with the scoring function. In this regard, a comprehensive and systematic analysis of various scoring functions and a systematic determination of cut-off value are required. Furthermore, SymD can also provide information about repeating units in a symmetric protein.In this regard, SymD outputs the N − 3 permuted structures and finds the best non-self-structural alignment between the original and each of the N − 3 permuted structures. Each of the iterations in the alignment scan procedure is independent and thus SymD algorithm is well suited for parallelization. Here, an iteration consists of finding the best non-self-structural alignment between the original and each of the N − 3 permuted structures and calculating optimal structure-based sequence alignment from the superimposed structures using the SE algorithm [At the heart of the SymD algorithm is the “alignment scan” procedure that aligns the original structure with each of the lgorithm  and thenParallelization of the SymD algorithm should be accomplished in such a way that the algorithm should automatically be able to share the workload equally among the available number of processors. If we increase the number of processors, it should again redistribute the workload so that each processor will have the same amount of workload. independent for each permutation, and thus the algorithm is appropriate for parallelization. Parallelization can also be achieved on the level of single computing node but such approach is not effective on parallel and distributed computing platforms with a lot of interconnected nodes. Because the alignment scan procedure implies high ratio between computation time and communication time, it can be better parallelized on a task level [Parallelization of the time-consuming alignment scan should be accomplished in such a way that it is equally efficient in single or parallel computing systems. This implies that the parallelization methodology must incorporate automatic balancing of computation. The alignment scan between original structure and the copy of circularly permuted structure of itself issk level . The ParA parallel platform can be represented as a set of slave nodes with a single master node. Bookkeeping is implemented as a separate process that runs on the master node. Usually, the master bookkeeping process is much simpler that the alignment scan processes and master node can run concurrently with slave nodes.This can be achieved by removing any hard coded area that restricts the number of processors and workloads from being shared. Instead of hard coding at the compilation time, we implement the program according to the following guidelines: (a) decide at runtime how and among how many processes should the workload be distributed; (b) as soon as a new processor is available, automatically redistribute the work share among the newly available resources.Obviously, one must check that SymD and Parallel-SymD yield the same results. To achieve this, we start by assigning the roles for each processor as a master or slave and design our program in such a way that even though the computations are done in individual processor, the results are sent back to one place and final results are shown by one processor.Z-score for the alignment scans to the master node.For accomplishing this, we assign the role of housekeeping to the master processor . The master processor will handle the decomposition of tasks into subtasks and mapping of those subtasks to other processors, collecting the results from each processor, and analyzing those local results and generating the final answer. In this regard, an iteration of “alignment scan” is run on slave nodes. The communication between processes is implemented using a standard MPI library and consists of master node sending the “specific number of iterations of alignment scan” to the slave node that will return the best As soon as the master processor gets the input and the information about the total number of processors, it reads the input protein and calculates the total number of residues in the input protein. Based on the number of processors available, the master processor then decomposes the task. If the residues lengths are not exactly divisible by the number of processes, the last processes as per the rank will be loaded with little extra work. After the decomposition of the task into subtasks, the master processor goes on a waiting phase where it seeks results from each processor. Sometimes, a process may take a long time to carry out execution and another process may complete the execution in a short time. But process 0 will not continue executing and analyzing the results until and unless it receives the result from each processor.N − 3 alignment scores, it displays all the results. Finally, a protein is considered symmetric if the Z-score of the best of these alignments exceeds a certain cut-off value.Once the master processor collects all the The program that activates the SymD algorithm is the same for all computing slave nodes and has two parts: first for supervising master process with process identification (ID) = 0 and second for the remaining of the slave processes. The communication between parallel processes is implemented using standard MPI library  and consAs the MPI is available in a standard form for most existing platforms , our appTo validate and assess the performance of the Parallel-SymD algorithm, we performed a series of computational experiments that are presented below. The computational experiments were performed on a CRAY XC30 system. This parallel computer system is composed of 6 CPU quad-socket 8-core Intel nodes and two CPU/GPU 10-core Intel nodes with 4 K40 NVidia GPU. The operating system is Linux Kernel, CRAY CLE based on SuSE SLES release 11.3. MPICH (version 2) was used and CRAY compilers were used to compile the source program. The nodes are connected with Aries Interconnect. Please refer to  for the Z-scores using the SymD [Z-score for a set of 2000 proteins which validates the notion that Parallel-SymD and SymD produce the same results. We also show in In order to validate that the Parallel-SymD algorithm works, we ran a set of 2000 proteins of various lengths and compared the the SymD  and ParaPerformance gain depends upon various factors like communication time , computation time , and protein size. If the protein size is increased, the communication time increases for a given processors number. For example, if we choose to use 20 processors, the communication time increases as we keep increasing the protein size.A plot of communication time versus the number of residues of proteins for 20 processors is shown in We also examined the effects of increasing the protein size on the computation time of calculating the symmetricity of a given protein. We observed that computation time shows quadratic growth as the protein size increases when the processor count is kept constant (= 20). The results of this analysis are shown in Apart from the number of residues or protein size, the communication time and computation time are also dependent on the number of processors used. If we vary the number of processors keeping the protein size constant, we could see how the communication time increases and computation time decreases as we increase the processor count.Next, we measured the speedup of the proposed parallelization on a number of proteins taken from ASTRAL40 dataset . We haveT1 of the nonparallel version of the algorithm. Then, we ran the Parallel-SymD algorithm on 20, 30, 40, 50, 60, 70, 80, and 100 processors of a CRAY XC30-AC parallel computer system. We measured the execution time of Parallel-SymD Tp and calculated speedup [S = T1/Tp. The maximum theoretical speed also known as ideal speed is equal to (p − 1) since only p − 1 processors are involved in performing the computation.We first ran the SymD algorithm on a single processor to find out the execution time  speedup  as S = TWe plotted the performance gain (speedup) when Parallel-SymD is run on different numbers of processors ranging from 20 to 80. The results are shown in Figures The advantage of the proposed Parallel-SymD is its ease of use and better performance, compared to naive parallelization. The only requirement is that the Parallel-SymD program is properly installed and that the master node is able to communicate with all slave nodes through the MPI library.We also plotted the time taken by the master node from the moment it receives data from the slave node to the point when it displays the result, postprocessing time. Since a single processor does the postprocessing, we have plotted the graph of postprocessing time against the protein size in In this work, we presented a parallel strategy to determine internal symmetry in a protein called Parallel-SymD. As the number of proteins structures continues to grow, it becomes very important to understand and characterize the structural features of these proteins including internal symmetry of the protein. The proposed parallel algorithm has been implemented on distributed computing system environment. The experimental results show that the algorithm presents good scalability and a nearly linear speedup. With the use of 100 processing nodes, the system achieved a 65x speedup. Thus, the proposed parallel algorithm scales well with the number of processors, enabling high performance on parallel systems.As observed from We have also characterized speedup of the algorithm by running the algorithm using 20, 30, 40, 50, 60, 70, 80, and 100 processors. Although we were not able to achieve the ideal speedup in each case  , however, we were able to achieve a nearly linear speedup. To be precise, in the case of 20 processors, we achieved performance gain closer to 81% of the ideal value. Similarly, in the case of 80 processors, we achieved performance gain closer to 74% of the ideal value.Finally, Parallel-SymD approach is one of the first types of approaches to detect symmetricity in proteins which harnesses the massive parallel architecture of existing computational infrastructures. To facilitate the use of Parallel-SymD source code, an executable of the program can be obtained from the corresponding author.


Protein interactions shape proteome function and thus biology. Identification of protein interactions is a major goal in molecular biology, but biochemical methods, although improving, remain limited in coverage and accuracy. Whereas computational predictions can guide biochemical experiments, low validation rates of predictions remain a major limitation. Here, we investigated computational methods in the prediction of a specific type of interaction, the inhibitory interactions between proteases and their inhibitors. Proteases generate thousands of proteoforms that dynamically shape the functional state of proteomes. Despite the important regulatory role of proteases, knowledge of their inhibitors remains largely incomplete with the vast majority of proteases lacking an annotated inhibitor. To link inhibitors to their target proteases on a large scale, we applied computational methods to predict inhibitory interactions between proteases and their inhibitors based on complementary data, including coexpression, phylogenetic similarity, structural information, co-annotation, and colocalization, and also surveyed general protein interaction networks for potential inhibitory interactions. In testing nine predicted interactions biochemically, we validated the inhibition of kallikrein 5 by serpin B12. Despite the use of a wide array of complementary data, we found a high false positive rate of computational predictions in biochemical follow-up. Based on a protease-specific definition of true negatives derived from the biochemical classification of proteases and inhibitors, we analyzed prediction accuracy of individual features, thereby we identified feature-specific limitations, which also affected general protein interaction prediction methods. Interestingly, proteases were often not coexpressed with most of their functional inhibitors, contrary to what is commonly assumed and extrapolated predominantly from cell culture experiments. Predictions of inhibitory interactions were indeed more challenging than predictions of nonproteolytic and noninhibitory interactions. In summary, we describe a novel and well-defined but difficult protein interaction prediction task and thereby highlight limitations of computational interaction prediction methods. Identification of protein interactions is an important goal in molecular biology yet one that remains difficult. Approaches such as yeast-2-hybrid, coimmunoprecipitation and newer experimental methods , 2 are h5DK noncovalent interactions (low nm or pm) and hence, in principle, being more tractable for identification than high-DK noncovalent, general protein–protein interactions. Previous cell culture and transcript analyses have suggested that known protease–inhibitor pairs are often coexpressed and coregulated  or interactions between proteins localized to different cellular compartments according to annotation (DK (∼ mK) . To evanotation . An advanotation . FamilieD (∼ Km) , 23. A fn = 294) as those inhibitions annotated in MEROPS  as enzymatically implausible inhibitor/protease pairs that are known not to be inhibitory. Using this gold standard, we evaluated the predictive power of common interaction prediction methodology to predict protease–inhibitor pairs in the protease web. Predictions were based on protein–protein interaction data, coannotation, coexpression, phylogenetic similarity, and colocalization as input data. Interestingly, we report that coexpression is surprisingly low for many functional protease–inhibitor pairs, contrary to what is commonly assumed. In particular, we employed 40 interaction predictors based on coexpression values derived from different input data and correlation metrics, all of which we found suffered from weak predictive power. Nonetheless, we predicted 270 protease–inhibitor pairs, examined 9 of these predicted inhibitions biochemically, and validated the novel inhibition of kallikrein 5 (KLK5) by serpin B12 (SERPINB12), previously an orphan inhibitor.Here, we defined TP inhibitions    version Protein-protein interaction (PPI) data from Human Integrated Protein-Protein Interaction Reference  version hgu95av2.db package in R  data  were dowion in R . Partialcor package in R (entropy package in R) for all pairs of genes. The Bits network was constructed by multiplying InParanoid scores with the bit score for each cluster and the calculating Pearson correlation (cor package in R).Phylogenetic profile data were constructed by downloading mappings from human proteins to other species from InParanoid . Mappingage in R ) or mutucaret package. 60% of pairs were used for training and 40% for testing. Parameters picked by cross-validation were mtry of 2 for random forest and C of 0.1 and sigma of 0.2 for the radial support vector machine.Machine learning algorithms were run using the Coagulation factor 11 (F11), coagulation factor 12 (F12), plasma kallikrein (KLKB1), and the chromogenic substrates for F11 , F12 , and for KLKB1 (S2302) were from DiaPharma. Chromogenic substrates were measured at an emission wavelength of 405 nm as recommended by the suppliers. KLK5  and its quenched fluorescent substrate  and kallikrein 5  and its quenched fluorescent substrate  were from R&D Systems. Cleavage of quenched fluorescent substrates was measured using excitation/emission wavelengths of 380/460 nm for KLK5 and 320/405 nm for KLK7 as recommended by the suppliers. SERPINB12 was kindly provided by Dr. G. A. Silverman ; serpin A4 was kindly provided by Dr. J. Chao ; murine serpin B8 was from Sino Biological, Inc. ; and serpin B7 was from Creative BioMart . Protease activity was measured after incubation for 1 h at 37 °C with and without serpins in a POLARstar OPTIMA plate reader (BMG Labtech). Substrate cleavage and protease inhibition assays were also analyzed by 10% SDS-PAGE and silver staining of proteins after incubation at a 1:1 ratio protease:inhibitor (w/w) for 1 h at 37 °C.Our results are organized around a presentation of our investigation of the predictive power of each of several data types we considered. We then describe a combined prediction approach that attempts to improve predictive power, biochemical validation of selected predictions, and finally a more in-depth investigation of coexpression patterns of proteases and inhibitors.supplemental Table S1) and BioGRID , and a literature-curated PPI network . Comparing 559 annotated cleavage and 325 inhibition interactions between proteases (including inactive proteases) and inhibitors from MEROPS to PPI data from HIPPIE .Our goal in considering general PPI data was to identify any relevant interactions that are not included in MEROPS. We analyzed PPI networks of proteases and their inhibitors from the databases HIPPIE  . In 28 cases (29%), an inhibition of protease activity was observed in the original paper; in 20 (21%), an inhibition was inferred from complex formation in the source publication; and 14 (15%) interactions were based on a cleavage event of the inhibitor. Taken together, 62 (65%) of the 96 interactions were known protease web interactions that were simply not annotated in MEROPS, confirming the incomplete status of protease and protease inhibitor annotations reported previously (Supplementary Results). Thus, PPI data not only reflect known protease interactions but also are potentially useful to predict novel inhibitory interactions. To identify additional inhibitory interactions, we further analyzed a BioGRID-derived  and a literature-curated PPI network . However, these networks were subject to study biases as described previously (Supplementary Results). These results motivated us to consider additional data types for inhibitory interaction prediction.We investigated the possibility that novel protease inhibitors of proteases might be hidden in the high-throughput PPI screens, which identify thousands of interactions but often without functional follow up. We collected 96 protease–inhibitor PPIs not already annotated as inhibitory interactions in MEROPS and examined all the original publications that served as references for these interactions in HIPPIE  or are specific to one or two tissues , matching with known targets such as coagulation proteins and kallikreins . As demonstrated in supplemental Fig. S4, Pearson correlation , captures tissue-specific expression patterns whereas Spearman correlation requires correlation across all tissues. We therefore generated coexpression matrices using both measures on the entire GTEx dataset . In addition, to simultaneously capture both patterns of coexpression, we generated a matrix using the maximum of Pearson and Spearman correlation coefficients for each pairs of proteins (GTEX_All_Max). We then generated coexpression matrices  using partial correlation, which might help resolve complex correlation patterns between multiple variables (genes). To capture tissue-specific correlation, we generated coexpression matrices in subsets of GTEx limited to only one tissue and one matrix representing the average of the tissue-specific matrices (each both for Pearson and Spearman correlation). For comparison, we also measured Pearson and Spearman correlation coefficients in a large dataset based on mRNA microarrays . Finally, with the aim of deriving a more robust coexpression result, we performed a meta-analysis  across all tissues and in a tissue-specific manner in two ways: (i) datasets were merged into one large dataset (Merged) and (ii) Pearson correlation coefficients were obtained from each dataset and then averaged for each gene pair .Supplementary Results). Coexpression values between some matrices were highly correlated overall (matrices GTEX_All_Pcc and GTEX_All_Scc with r = 0.67) as shown in top right corner). Predictive power thus needs to be assessed separately for each matrix.The resulting coexpression matrices differed strongly in content depending on the methods and data used . TP pairs thus had higher coexpression than TN pairs on average. However, considering the common perception that protease inhibitors are coexpressed with their target protease, this signal was surprisingly low (AUCs < 0.7). One explanation for this discrepancy might be that RNA levels do not correspond to protein levels, and thus proteins can be coexpressed whereas their mRNAs are not. We tested this possibility by creating a coexpression network based on proteomics quantification data in the Human Proteome Map (We compared the ability of coexpression matrices to predict protease inhibition (inhibitory protease–inhibitor pairs). We measured the area under the curve (AUC) of the receiver-operator characteristic for separating predefined TP pairs (annotated) from TN protease–inhibitor pairs  using a given coexpression matrix. Fig. 5eome Map , but thiB). Here, Array_Averaged_Liver and GTEX_All_Max methodologies had the highest signal. The strong correlation of Array_Averaged_Liver with protease web data was expected because many interactions are known between proteases and inhibitors that are expressed in liver as part of the well-understood complement and coagulation systems. However, this network is therefore biased to a subset of the protease web and performance would not generalize to other genes. The high AUC and accuracy of GTEX_All_Max demonstrated the usefulness this network, which combined tissue specific and across-tissue coexpression, making it the better candidate for prediction.Similar results were found when measuring accuracy of interaction prediction within the top 10% of coexpressed pairs of each matrix B. Here, supplemental Table S5). The resulting matrices were very similar to each other (correlation 0.7–0.98), but dissimilar from the coexpression matrices (B).Phylogenetic similarity of two genes is a measure for their co-occurrence across a range of taxa and is reported to reflect functional relations . We creamatrices , indicatmatrices B.We next evaluated subcellular colocalization, which might enrich for interacting proteins. We obtained localization annotations LocDB , the Humde novo predictions, where novel interactions or functions are predicted, and mere retrieval of information already present in the literature but not yet annotated to databases . Therefore, we focused on the best performing individual matrix for prediction, GTEX_Max, selecting a coexpression threshold of 0.6. This threshold enriching TP 5.5-fold in comparison to TN as shown by TP to TN ratio  it is known that the inhibitor blocks a protease from the same family as the predicted target protease or (ii) it is known that inhibition of the protease occurs by an inhibitor from the same family as the predicted cognate inhibitor. These two filters reduced the search space from 1,239 coexpressed pairs to 270 pairs. We anticipated that the incorporation of enzymatic constraint would greatly increase the precision of predictions. A loss of sensitivity is possible if all target protease families of an inhibitor or all inhibitor family members of a target protease are not annotated as such, but we considered this unlikely since relevant inhibitor families are known for most proteases. Inhibitor–protease pairs meeting these criteria are shown in To evaluate the predicted protease–inhibitor pairs, we selected a number of pairs for biochemical experiments: inhibition of factor XI (F11), factor XII (F12), and KLKB1 by kallistatin (SERPINA4) (three pairs) as well as inhibition of KLK5 and kallikrein 7 (KLK7) by serpins B7, B8, and B12 (six pairs). It is important to note that we evaluated predictions without biases resulting from selecting the most likely pairs. Instead, we selected pairs of biologically interest and availability of reagents. GTEx showed high expression of kallistatin in liver together with the coagulation proteases factor XI, factor XII, and plasma kallikrein. Whereas this indicated an interesting new role for kallistatin, the predictions were indeed risky since all of the four proteins are well studied and newly discovered inhibition is therefore unlikely. Moreover, liver is known to express and secrete many serum proteases and inhibitors, and so there was additional likelihood that these predicted pairs would not be true. On the other hand, serpins B7, B8, and B12 are little studied inhibitors and thus biologically interesting interaction partners of KLK5 and KLK7.in vitro. We found no new targets for kallistatin among the three serum proteases but did observe inhibition of KLK5 by SERPINB12 (A), which we confirmed by analysis of covalent complex formation on SDS-polyacrylamide gels (B). Given that these proteins are coexpressed and therefore found in the same tissue, we conclude that this interaction is also likely to be physiologically relevant. The interaction could also represent an interesting drug target since KLK5 is a major regulator in a number of diseases . We also tested whether a more comprehensive dataset would improve predictions by basing our predictions using updated versions of GTEx  and MEROPS (version 11.0) but did not see any improvement . To further understand the low correlation between coexpression and the protease inhibition, we investigated coexpression of exemplary, well-studied inhibitor–protease pairs. First, we examined serpins that regulate coagulation (e.g. antithrombin III (SERPINC1) and alpha-2-antiplasmin (SERPINF2)). Indeed, these serpins were coexpressed with coagulation proteases in liver, which, as noted above, is the case for many serum proteins destined for secretion to the circulation. However, these serpins are annotated to inhibit many additional proteases. For example, SERPINF2 inhibits KLK 4, 5, 7, 13, and 14 that are not highly expressed in liver, have uncorrelated expression patterns from SERPINF2 (A). Yet, these interactions are biochemically meaningful, as serpins are exported from the liver to the serum and thus transported through the body, where they encounter and inhibit proteases with uncorrelated expression patterns. Thus, as we showed before, coexpression in exocrine tissues is limited in predicting such protein interactions.We were intrigued by the low coexpression of protease–inhibitor pairs  and the SERPINF2  and are SERPINF2 A. Yet, tB) was not coexpressed with any of its annotated target proteases with a few exceptions (MMP2 or MMP14), which were drowned out by the number of other annotated targets. In the extreme case, alpha-2-macroglobulin (C), a highly multifunctional protease inhibitor that inhibits multiple classes of proteases, is also only coexpressed with a small portion of proteases. However, alpha-2-macroglobulin is present in blood plasma and so can reach most tissues  (23) and inhibit extracellular proteases or intracellular proteases that are secreted (e.g. by neutrophils (e.g. serine proteases and MMPs)) or released from damaged cells. These examples show that expression patterns of inhibitors are often correlated with the expression pattern of some (possibly the most relevant) targets but uncorrelated with additional biologically relevant targets.These observations were not limited to serpins but applied to all groups of inhibitors: For example, TIMP 2 B was notglobulin C, a highWe compared the performance of our coexpression matrices in predicting protease–inhibitor interactions to the performance achieved when predicting general protein interactions in the HIPPIE network , using ie.g. SERPIND1 and SERPINC1). Focusing on interactions between inhibitors and proteases (predicted probability of 0.9 or higher), we identified 241 predictions. 41 interactions overlapped with our 270 predictions despite the use of different methodology and underlying data. It is not possible to determine the accuracy of PrePPI predictions since PrePPI uses structural constraints and GO annotations, which partially correspond to the annotations we used to build the gold standard. However, despite these filters, 7 of the 51 interactions of serpins included metalloproteases, and none of the eight predicted interactions of cystatins included cysteine proteases, indicating that these predictions lack specificity and are not easily applicable to the prediction of inhibitory protease interactions.To assess whether predictions of generic protein interactions in state-of-the-art methodology are useful in predicting protease inhibitions, we obtained predicted interaction partners of protease inhibitors from PrePPI . Withoutvice versa, the expression pattern of one gene represents a combination of the patterns of its interactors and is thus not clearly correlated with any one of them individually. However, our attempt to address this by using partial correlations did not provide support for this notion.We observed that coexpression was very limited in utility in predicting protease–inhibitor pairs, contrary to previous findings suggesting proteases and their inhibitors have correlated expression  and 15. In addition to many true inhibitor–protease pairs not coexpressing, we also found that coexpressed pairs are often not inhibitory. We hypothesize that this is due to pathways of genes, where a gene can be coexpressed with other genes in the same pathway that are not direct interactors. In our data, one example is the coexpression of SERPINB12 with KLK5 and KLK7. SERPINB12 inhibits KLK5 but not KLK7. KLK5 is also an activator of KLK7 . Whereasde novo predictions, and were hand selected for validation based on plausibility, which can significantly bias performance evaluation. Therefore, the reported performance for PrePPI is likely overoptimistic. Indeed, in a recent mass spectrometry screen of interaction partners of adenosine monophosphate-activated protein kinase-α1 and -β1 (Due to the complexity of biological networks, identification of biochemically relevant protein interactions remains an intricate problem. Prediction of inhibitors of proteases provided in-depth insights into the related difficulties since it allows a cleaner definition of TP and TN examples than general protein interaction prediction. We emphasize that biochemical evaluations are useful to give realistic estimates of expected performance because computational evaluation such as cross-validation performance is a very poor predictor of how a guilt-by-association method will do in reality . Our per and -β1 , only 63We also revealed strengths and weaknesses of individual input features in our focused analysis. One important input feature was the prior knowledge of structural constraints, namely the knowledge of protease and inhibitor classes. We hypothesized that this feature would greatly simplify the inhibitor prediction task by narrowing and focusing the search space compared with the general protein interaction prediction problem. Without enzymatic constraints, common protein–protein interaction predictions included many irrelevant predictions, as we demonstrated in the case of PrePPI. In our analysis, protease-specific structural features significantly reduced the search space to relevant interactions but also did not discern specificity between closely related protein family members. We conclude that such structural information as well as the related sequence-based input used in phylogenetic similarity decreases the search space to plausible pairs but is not specific enough to identify individual interactions.Overall, we conclude that computational interaction prediction remains challenging and with the current state of data, and methods seem unable to accomplish the task with sufficient specificity to reliably replace biochemical experiments. Improvements may come from more comprehensive expression studies and proteomics quantification as well as a better definition and larger numbers of TP and TN examples used for training to identify pertinent patterns. This will improve as more data are uploaded to community databases such as MEROPS and TopFIND for proteases and inhibitors. We caution against overoptimistic estimates of performance based on aggregating diverse data in a black box algorithm and overtrusting cross-validations based on flawed gold standards , 45. Equhttp://hdl.handle.net/11272/10472.Protease and protease inhibitor data and coexpression matrices used throughout the analyses are available for download at


Based on phylogenetic profiling, here we present a highly scalable and time-efficient computational framework for predicting linkages within the whole human proteome. We have validated this method through analysis of 3,697 human pathways and molecular complexes and a comparison of our results with the prediction outcomes of previously published co-occurrency model-based and normalization methods. Here we also introduce PrePhyloPro, a web-based software that uses our method for accurately predicting proteome-wide linkages. We present data on interactions of human mitochondrial proteins, verifying the performance of this software. PrePhyloPro is freely available at  The development of sequencing technologies has facilitated the access to whole genomic information from numerous organisms. Despite successful small-scale attempts to identify protein–protein interactions in a limited number of model organisms , determiBased on occurrence information across different species, two main groups of prediction algorithms have been proposed . In the Eukarya phylogenetic profiles have been developed , Pearson correlation coefficient (“Cor”), mutual information (“MI”) and Hamming distance (“Hamming”), relied on the evolutionary similarity or distance . The secx- and y-axis, respectively. A larger area under the curve (AUC) of ROC would indicate better reliability of the method. We observed that the AUC of PPP was the largest (0.73) in comparison to other conventional approaches. “Jaccard” had the third largest AUC (0.71), as this coefficient was one of the important tools used in PPP. In comparison to “Jaccard”, PPP had enhanced sensitivity upon increasing the FPR. For example, by changing the FPR to 0.20, the sensitivity of “Jaccard” was 0.46, whereas the sensitivity of PPP increased to 0.53, showing a noticeable improvement  and true positive rate  were calculated and represented in the rovement . “Cor” arovement . To deterovement . Our resRecent studies have suggested that the model-based methods have higher discriminative power and better performance . Other sz-score and truncated unitary matrix with “Cor” and Euclidean distance, respectively. Our results showed that NPP achieved a significantly better performance than individual “Cor” correlation measures that had the second largest AUC (0.72). Similarly, setting the top percentage of unitary matrix as 30% (“SVD30”), would result in a higher AUC than that of the Lp-norm based methods, including “Hamming” , BioCarta, Reactome and NCI/Nature Pathway Interaction Database (NCI), for pathways analysis and CORUM to establish connections of proteins within complexes. The list included known linkages in 241, 247, 1,393 and 212 pathways in the KEGG, BioCarta, Reactome and NCI databases, respectively. We also included 1,604 different complexes through CORUM . With thMetazoa while ARHGEF9 homologs were also detected in Fungi  protein-protein interactions, for predicting new components of biological complexes, and suggesting potential new linkages in signaling pathways or metabolic processes.E-value threshold, and the reference organisms. The top rank is the number of linkages  corresponding to each queried protein. In the front page three options  are provided for the BLAST E-value threshold. This threshold is applied to choose homologies among the 972 species in constructing phylogenetic profiles. Using a smaller top rank and BLAST E-value would result in less but more reliable linkages. Currently the PrePhyloPro software provides linkages prediction in two model organisms, Homo sapiens and Arabidopsis thaliana. Prediction in more species will be available, as future updates will be applied to the software. The second step is to set the size of protein names for the output plots as the default setting may not be suitable for studies with high or low number of queried proteins. In the last step, a table of query proteins in the “txt” or “csv” format will be uploaded onto the website for linkage prediction. The markup colours and aliases for query proteins could be added to this table to optimize visualization of output figures. Examples of input files are provided online.PrePhyloPro is designed as a user-friendly tool that requires three steps. The first step is choosing algorithm parameters including the top rank threshold, BLAST E-value thresholds, and have saved them in backend databases. In total, PrePhyloPro takes less than 1 min to determine whole proteome search for linkages of 20 candidate (query) proteins. PrePhyloPro returns an integrated webpage including output figures and tables. As an example of an input protein set, we have used subunits of the human F1Fo ATP synthase . The left panel represents the input proteins (marked with user-defined colours) combined with a cluster dendrogram measured by Euclidean distances. The blue and gray bars in the phylogenetic profile plot correspond to presence or absence of homologies, respectively  were displayed in Bacteria, Archaea, and Eukaryota (from the outer to the inner ring). Hovering over the points will show the corresponding present percentages. In the centre, connecting arcs represent the predicted links for the 6 subunits in F1 ATP synthase with user-defined colours , and the sort option (at the top of each column) . Output V-type ATP synthase, was also identified by PrePhyloPro. These two proteins are believed to have evolved from the same ancestor by gene duplication  using SVD , PhyloGeE-value as the criteria, in which 1 denoted that homologies of human proteins found in the corresponding species, otherwise 0.Protein sequences and annotation information from 972 different species were retrieved from the KEGG database , includin species, for example Four independent co-occurrence methods were used to evaluate the correlated relationship between a pair of proteins . For eacX, and Sx is sample standard deviations of X.The Pearson correlation coefficient is: X.The mutual information is: Lp-norm is defined as: p = 1 is the Hamming distance.The Eukaryota, 201 in Bacteria, and 78 in Archaea. The truncated ribosomal RNA sequences were aligned using the MAFFT program , we set this parameter as 100% and 30%. Similar to the second step of NPP, poor conserved proteins were marked in the original profile and removed in P′ as P′ may have less organisms and proteins than those in the original profile P.Another normalization method was called SVD . In a bip = 2 in The Pearson correlation coefficient and Euclidean distance p = 2 in  were appT linkages for each protein.We combined co-occurrences to implement a new method named PPP to improve the prediction efficiency. PPP was inspired by “solo proteins” that did not link to any other proteins, when we conducted whole proteome linkages prediction. However, solo proteins may be not in existence, considering the huge size of protein interactions representing complex biological activities . One soln proteins from a phylogenetic profile across m species. The first step was to roughly exclude linkages with negative correlations. For a given protein ni, the Pearson correlation with the other proteins was calculated and denoted as VJ was generated. We excluded proteins with negative Pearson correlations, because a negative number would imply the presence of one protein and the absence of another in a pair, which could not be considered as a functional link under an evolutionary scenario. In the next step, we recorded the rank in decreasing order of each element in the vector VJ and denoted as T ranges from 1 to n − 1. Thus, for a given protein, it has at least one partner owning the largest co-occurrency. A smaller T yielded less but more reliable linkages.We illustrated PPP by predicting whole proteome linkages among The whole proteome functional linkages were calculated for proteins as the same procedural We retrieved 1,604 human complexes from the CORUM database . To genex-axis and y-axis, respectively, as: Similarities or distances in co-occurrence and normalization methods, LRs in the maximum likelihood method, distances in the Dollo parsimony method, and top rank in PPP were used to generate series of thresholds. Under each threshold, the number of TP true positives (TP) and true negative (TN) represented the positive and negative predicted linkages, respectively. In contrast, false positive (FP) and false negative (FN) were erroneously detected as positive and negative linkages under certain thresholds. In ROC curves, we represented the FPR and TPR in the P and N represent the total number of positive and negative reference links, respectively. In PR curves, we defined the precision and recall as: Ntop∕NMMM, where Ntop denoted the number of PPP predicted linkages under certain top rank threshold  and NMMM was the number of MMM re-generated protein pairs.To validate the linked proteins that were present across a wide range of species, we compared the PPP predicted linkages with the results generated by the MMM method, which evolves the largest common submatrix between paired proteins . With thTo evaluate the biological features of our whole proteome predicted functional linkages, we chose four different biological gene-sets databases: KEGG , BiocartTo generate the human mitochondria correlation network with our prediction results, we retrieved 1,006 human mitochondria related proteins , which whttp://www.r-project.org/).The pROC package was used to perform the typical ROC analysis . The SVD10.7717/peerj.3712/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.3712/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.3712/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.3712/supp-4Figure S1ROC curves and PR curves are generated by using another random negative reference dataset (A–B), complexes with less than 40 subunits (C–D), and a validation dataset from Ta and colleagues  (E–F).Click here for additional data file.10.7717/peerj.3712/supp-5Figure S2The protein pairs with more homology across a wide range of species showed a higher hit rate at more stringent top ranks, indicating increased true linkage detection by PPP. The hit rate denotes the ratio between the number of linkages predicted by PPP and the number of MMM linkages.Click here for additional data file.10.7717/peerj.3712/supp-6Figure S3The two clusters consist of a group of proteins present among a wide range of species and a group that was almost exclusively present in Metazoa.Click here for additional data file.


The strength and specificity of protein complex formation is crucial for most life processes and is determined by interactions between residues in the binding partners. Double-mutant cycle analysis provides a strategy for studying the energetic coupling between amino acids at the interfaces of such complexes. Here we show that these pairwise interaction energies can be determined from a single high-resolution native mass spectrum by measuring the intensities of the complexes formed by the two wild-type proteins, the complex of each wild-type protein with a mutant protein, and the complex of the two mutant proteins. This native mass spectrometry approach, which obviates the need for error-prone measurements of binding constants, can provide information regarding multiple interactions in a single spectrum much like nuclear Overhauser effects (NOEs) in nuclear magnetic resonance. Importantly, our results show that specific inter-protein contacts in solution are maintained in the gas phase. Double mutant cycle (DMC) analyses can provide the interaction energies between amino acids at the interface of protein complexes. Here, the authors determine pairwise interaction energies using high-resolution native mass spectroscopy, offering a straightforward route for the DMC methodology. In this method, two residues of interest are mutated individually and in combination and the energetic effects of the mutations  are measured. If the effect of the double mutation differs from the sum of effects of the corresponding single mutations then an interaction, either direct or indirect, between the two residues is inferred. Under favorable circumstances, the deviation from additivity, termed the coupling energy, can be used to estimate the strength of intramolecular3 and intermolecular4 pairwise interactions in proteins or protein–ligand complexes. Coupling energies can also be used as constraints (much like nuclear Overhauser effects (NOEs) in nuclear magnetic resonance) to determine protein structures, e.g., when docking two proteins to each other5. Recent applications of the DMC method in molecular recognition studies include combining it with cryo-electron microscopy to identify the binding site of capsaicin in the TRPV1 ion channel6 and demonstrating an affinity-specificity tradeoff in PDZ–peptide interactions7. Here we describe a native mass spectrometry-based approach that simplifies the application and enhances the power of DMC analysis for studying protein interactions. Our results also show that specific inter-residue interactions are maintained in the gas phase.Methods such as X-ray crystallography, electron microscopy, and nuclear magnetic resonance can be used to determine the structures of protein complexes at high resolution. Nevertheless, complementary methods are needed for determining the energetics of binding reactions and for obtaining structural information in cases that are not amenable to analysis by these methods owing, e.g., to low solubility, large size, and/or high flexibility. One such complementary method that is widely used is double-mutant cycle (DMC) analysisi and Yj, and the two corresponding single mutants, Xm and Ym, . Likewise, the change in the binding free energy upon the mutation i→m, when residue j has already been mutated to m, may be expressed as ΔG(im→mm). The difference between ΔG(ij→mj) and ΔG(im→mm), which is equal to the difference between ΔG(ij→im) and ΔG(mj→mm), provides a measure of the coupling energy, ΔΔGint(ij), between residue i in protein X and residue j in protein Y. This coupling energy can be expressed in terms of binding constants as follows:Kij is the binding constant for the two wild-type proteins, Xi and Yj, Kmm is the binding constant for the two corresponding single mutants Xm and Ym and Kim and Kmj are the respective binding constants for Xi with Ym and Xm with Yj.The application of DMCs for detecting and quantifying inter-protein pairwise interactions between residue i in protein X and residue j in protein Y involves creating a cycle that comprises the four possible complexes that can be formed by the two wild-type proteins, XYm, Fig. . The chaGint(ij). The native mass spectrometry (MS) approach for DMC analysis that is described here circumvents the need for the determination of the individual binding constants and provides a way to measure ΔΔGint(ij) directly from a single spectrum. It is based on the realization that substitution of each of the binding constants in Eq. Kij = [XiYj]/[Xi][Yj]) leads to the following expression:Identification and quantification of an inter-molecular pairwise interaction using the DMC approach is labor intensive since it requires generating binding isotherms for the four complexes in each cycle using methods such as isothermal calorimetry or surface plasmon resonance. It is also error-prone because unavoidable errors in protein concentrations lead to errors in the estimates of the binding constants and, thus, to a cumulative error in the estimate of the value of the coupling energy, ΔΔ12. The high resolution afforded by the Orbitrap platform enabled us to determine the intensities of all four complexes simultaneously from a single mass spectrum. Hence, by mixing the two wild-type proteins, Xi and Yj, and the two corresponding single mutants Xm and Ym, and then measuring the intensities of all four possible co-existing complexes using native MS one can obtain a direct estimate of ΔΔGint(ij) that does not depend on knowing the concentrations of the free species .Native MS is a method based on the ability to transfer protein complexes to the gas phase without disrupting them13. Two DMCs were constructed: one for the interaction of Asn34 in Im2 with Ser84 in E9 and a second for the interaction of Asp33 in Im2 with Phe86 in E9. Phe86 is a main hot spot residue in the interface of E9 with Im913. In both cycles, all the mutations were to alanine as before13. The coupling energies of the two pairwise interactions were determined by the native MS-based approach for DMC analysis described here and, in comparison, by measuring all the required individual binding constants using isothermal titration calorimetry.The applicability of this native MS-based approach for measuring pairwise coupling energies was tested for the well-characterized interaction of colicin E9 endonuclease with the bacterial immunity protein Im2 with which it forms a 1:1 complex14. This was confirmed by showing that the affinity of the E9 Ser84→Ala mutant for Im2 is unchanged by the tail mutations . The positive coupling energy of more than 1 kcal mol−1 found for Asp33 in Im2 and Phe86 in E9 is consistent with a stabilizing and possibly cooperative anion–aromatic interaction between the O and OD2 atoms of Asp33 and the respective Cz and CE1 atoms of Phe86, which are separated in the complex structure by about 4.3 and 5.5 Å, respectively14. Cation-pi pairs in proteins have been documented widely but much less attention has been paid to anion–aromatic interactions between negatively charged side chains and the positive electrostatic potential associated with the ring edge of aromatic groups16. The negative coupling energy of about −1.7 kcal mol−1 reported for the interaction of Asp33 in Im2 and Phe86 in E9 in previous work13 suggests that the different buffer conditions employed in the two studies may have caused conformational switching between a favorable (negatively charged side chain with ring edge) and unfavorable (negatively charged side chain with ring face) anion–aromatic interaction.Given the relative concentrations of the four complexes that form each of the cycles, it was possible to calculate the coupling energies corresponding to them using Eq. 12. Here we have shown using DMC analysis that the interaction energies of specific residue pairs can also remain unchanged upon transfer to the gas phase. The native MS version of DMC analysis described here provides values of coupling energies without the need for multiple measurements of affinity constants and accurate determination of protein concentrations, thereby simplifying the measurements and reducing the experimental error. The method can be extended to work in a high-throughput fashion by mixing wild type and n different single mutants of one protein with wild type and m different single mutants of a second interacting protein. These n + 1 and m + 1 variants of the two proteins form m × n different mutant cycles that can yield m × n coupling energies that can serve as constraints for docking. Hence, it might be possible in the future to determine the structure of a complex from a single native mass spectrum if the structures of the free proteins are known.In summary, many previous studies have shown that protein–ligand and protein–protein complexes can be maintained in the gas phase17. The following mutagenic oligonucleotides were used together with the T7 universal primer as forward and backward primers, respectively.The plasmid pET21d containing the wild-type genes of E9 DNase and Im9 with a His tag or E2 DNase and Im2 with a His tag was used as a template for mutagenesis by restriction-free cloningPhe86→Ala in E9:5′-GTGTTTCAAAAGGTTATTCTCCGGCTACTCCAAAGAATCAACAGGTCG-3′;Asn34→Ala in Im2:5′-GGTGCTACTGAAGAGGATGACGCTAAATTAGTGAGAGAGTTTGAGCG-3′;Asp33→Ala in Im2:5′-GGTGCTACTGAAGAGGATGCCAATAAATTAGTGAGAGAGTTTGAGC-3′. The mutation Ser84→Ala in E9 was introduced together with two other mutations, His131→Ala and Arg132→Ala, designed to increase the difference between the mass of this mutant and those of the other variants without affecting its binding to Im2. This triple mutant was created using the following respective mutagenic oligonucleotides as forward and backward primers.Ser84→Ala:5′-GGTGCTACTGAAGAGGATGCCAATAAATTAGTGAGAGAGTTTGAGC-3′;His131→Ala and Arg132→Ala:5′-CCTAAGCGACATATCGATATTGCCGCAGGTAAGTAAAATGGAACTGAAGC-3′. The mutations were confirmed by DNA sequencing of the entire genes.E. coli BL21 (DE3) cells harboring the pET21d plasmid that contains the appropriate genes coding for E9 and Im9 with a His6 tag or E2 and Im2 with a His6 tag were grown in LB medium containing 100 µg ml−1 ampicillin at 37 °C until an O.D600 nm of 0.6 was reached and protein expression was then induced by adding 1 mM isopropylthio-β-galactoside. The cells were then grown for another 4 h at 37 °C and harvested. Cell pellets were stored at −80 °C until further use. Purification was carried out by resuspending the cells in 20 mM Tris buffer (pH 7.5) containing 100 mM NaCl, 5 mM imidazole, and 1 mM DTT (buffer A) to which was added the cOmplete, EDTA-free, protease inhibitor cocktail . The cells were then disrupted by sonication and the lysate was clarified by centrifugation. The supernatant was loaded on a 5 ml HisTrap HP column . The E9 DNase was dissociated from the column-bound Im9 and eluted by washing the column with buffer A containing 6 M guanidine hydrochloride. Im2 was eluted using a 10–200 mM imidazole gradient in buffer A containing 6 M guanidine hydrochloride. Fractions that contain the E9 and Im2 proteins were dialyzed overnight at 4 °C against water and lyophilized. The purified E9 was stored at −80 °C until further use. The Im2 protein was dissolved in 50 mM Tris buffer (pH 7.5) containing 50 mM KCl and 1 mM DTT (buffer B) and then further purified using a Superdex 75 column equilibrated with this buffer. The fractions containing Im2 were then loaded on a 5 ml HiTrap Q FF column . The Im2 protein was eluted from the Q FF column using a 0.05–0.5 M KCl gradient in buffer B. Fractions containing Im2 were combined, dialyzed overnight at 4 °C against water, and then lyophilized. The purified Im2 protein was stored at −80 °C until further use. The concentrations of E9 and Im2 proteins were determined using molar absorbance coefficients of 15,470 and 10,095 M−1 cm−1, respectively.Experiments were carried out using an ITC200 Microcal calorimeter. Lyophilized protein samples were dissolved in 250 mM ethylenediammonium diacetate (EDDA) buffer (pH 7.0) and then dialyzed overnight at 4 °C against this buffer. The protein samples were centrifuged and their concentrations determined before starting the ITC experiments. The titrations were carried out using a concentration of 0.04–1 mM E9 in the cell and a ten-fold higher concentration of Im2 in the syringe. Each ITC experiment comprised 26 injections with 3 min intervals of 1.5 µl Im2 at 25 °C. The heats of dilution were subtracted from the raw data and values of the binding constants were then determined by fitting the resulting binding isotherm to a 1:1 binding model using the ORIGIN program supplied by the manufacturer.18 and then sprayed into the instrument. Conditions within the mass spectrometer were adjusted to preserve non-covalent interactions, with the source operating in positive mode. The following experimental parameters were used: capillary voltage, 1.7 kV; HCD direct voltage, 30–70 eV; and inlet capillary temperature in the range of 160–245 °C. MS spectra were recorded at a resolution of 17,500 (at m/z 200).All the native MS experiments were performed using a Q Exactive Plus Orbitrap mass spectrometer  modified for optimal transmission and detection of high molecular weight ions. Before MS analysis, proteins were dialyzed against 0.25 M EDDA buffer (pH 7). Proteins forming the DMC of interest were then mixed at the desired ratios and incubated for 5 min at room temperature. Typically, an aliquot of 2 μl protein solution was loaded into a gold-coated nano-ESI capillary prepared in-house as previously described+ and (M+C2H10N2)+, were calculated using the PeakFit v4.12 software . In this program, peaks are fitted automatically to a series of Gaussians using a deconvolution approach. A numerical fitting procedure was repeated to minimize the deviation from experimental data as monitored by the coefficient of determination, R2. Typical R2 values were higher than 0.99. This analysis was repeated for the two detected charge states, i.e., 8+ and 9+. The reliability of the data deconvolution results was validated by comparison of the expected peak position calculated from the mass to charge ratio with the generated peakfit value. In addition, in cases of uncertainty due to the presence of bound ethylenediammonium adducts, the assignment was further confirmed by comparing the relative abundance of a specific peak in measurements performed at different ratios. For example, the peak at 2889 m/z can correspond to the complex of E9S84A with either wild-type Im2 or Im2N34A. This peak increases when the concentration of wild-type Im2 is doubled relative to the other three proteins Data supporting the findings of this study are available within the article and its  file andSupplementary InformationPeer Review File


However, protein half-lives in cells and tissues are different. Due to the resource requirements for researching tissues, more data is available from cellular studies than tissues. We have designed a multivariate linear model for predicting protein half-life in tissue from its cellular properties. Inputs to the model are cellular half-life, abundance, intrinsically disordered sequences, and transcriptional and translational rates. Before the modeling, we determined substructures in the data using the relative distance from the regression line of the protein half-lives in tissues and cells, identifying three separate clusters. The model was trained on and applied to predict protein half-lives from murine liver, brain and heart tissues. In each tissue type we observed similar prediction patterns of protein half-lives. We found that the model provides the best results when there is a strong correlation between tissue and cell culture protein half-lives. Additionally, we clustered the protein half-lives to determine variations in correlation coefficients between the protein half-lives in the tissue versus in cell culture. The clusters identify strongly and weakly correlated protein half-lives, further improves the overall prediction and identifies sub groupings which exhibit specific characteristics. The model described herein, is generalizable to other data sets and has been implemented in a freely available R code.Protein half-life is an important feature of protein homeostasis (proteostasis). The increasing number of  However, this limitation is not present in cultured cells, allowing half-lives as short as one to two hours to potentially be measured. Therefore, computational techniques are needed to map the observations from cell lines to the corresponding tissues. Another challenge, though not addressed here, is that tissues are composed of different cell types, therefore requiring the combination of protein information from multiple cell types. In this study, we make a first attempt at predicting protein turnover rates in tissues from their cellular properties  as explanatory variables, and the protein half-life in tissue as the response variable. The model is trained on randomly selected data sets by minimizing an objective function that is associated with the predictive results–9. We haTo reduce the deviation between the protein half-lives in tissues and cells, we first determine substructures (clusters) in the protein half-lives (cellular and tissue) data. The clustering is based on the relative distance of a protein half-life from the linear regression line between in vivo data sets from murine liver[in vitro data sets from murine fibroblast (NIH3T3[in vivo experiment used Nitrogen-15 (15N) isotope labeling in the murine brain and liver study[in vitro[in vivo murine heart experiment[We used publicly available ine liver, brain[7ine liver, and in t (NIH3T3), and myt (NIH3T3) cell liver study study. Tver study. In thesver study). We hav[in vitro C2C12[13We developed a model using proteins which have longer half-lives in the tissue than in cell culture and are common in both . However, the model is also applicable to those common proteins that have shorter half-lives in the tissue than in cell culture see . For a fYt is the protein half-life in the tissue, Xt is protein half-life in the cell culture, m is the slope, and wt is the intercept of the linear model.The linear regression model has the following form:The half-lives of proteins are scattered around the linear regression line , demonstWe clustered the proteins based on strongly and weakly correlated protein half-lives in cells and tissues. The approach provided a systematic analytical perspective for clustering, while improving the prediction of protein half-lives in tissues. We found that the correlation coefficient is the highest for some common liver proteins, which are the nearest to the regression line , c ∈ {C1,C2,C3}) into three clusters , then one cluster is above 10% deviation . Each data set has the protein half-lives at tissue and cellular levels along with several other protein properties. We built a multivariate linear model[Cell_half-life, wP_length, wP_abundance, wI_sequence, wmRNA, wTranscription, wTranslation) ϵ , wc is the intercept from the linear regression ) of each cluster. Protein properties such as abundance (PA), mRNA level (MR), transcriptional (TR) and translational rates (TL) from the cell line study[e for consistency in the calculations . Each cluster  has its own protein half-life predictor  to minimAs a measure of performance of the model, we used the following percentage of error (PE) equation to measure the relative deviation of protein half-life prediction in tissue from the corresponding experimental value:The values of the correlation coefficients show that some of the protein properties are correlated either positively or negatively  with theWe are presenting the analysis of the performance of the model, the effect of clustering on the protein half-life prediction, and half-life prediction of uncommon proteins. The analysis focuses on the correlation coefficients between protein half-lives in the tissue and cell cultures, as the half-life clusters are formed based on the correlation coefficients. This also leads to the identification of common protein half-life characteristics of each cluster . We have1 has the largest intercept  have protein half-lives that deviate from the regression line . Additionally, clustering improves the overall prediction . It also provides an opportunity for protein half-life prediction for strongly correlated (C2) and weakly correlated proteins (C1 and C3) in the tissue and from the cell.On the other hand, Cine e.g. . For theata sets  Tables. 2, C3 of The cell culture protein half-life has the highest positive correlation with the tissue half-life than the rest of the protein properties of each cluster . Some pr3 has shorter protein half-lives and the smallest number of positively-correlated protein properties .This seems to indicate that the multivariate linear model needs larger number of protein properties for predicting weakly correlated protein half-lives. This observation is also true for the brain and heart data sets  have more ubiquitination sites  that is associated with the protein half-life in the cell in 2 and C3 in 2 in We have used the GA to minimize the objective function . The valGTranscription of C3 in GmRNA and wGP_abundance of C2 in 2 for common brain proteins   Fig 5) bWe calculated the probabilistic distribution of the clusters and used this while clustering the proteins with the neural network (NN). The black lines show the connections between each layer and the weights on each connection, while the blue lines show the bias term added in each step. The biaWe used the multivariate linear model and optimized weight vectors for each of the clusters for predicting the protein half-life of uncommon proteins . A clustWe have proposed a model for the proteins which have longer half-lives in the tissue than in the cell-line. We found that 84% proteins from the liver and heart protein data sets and 97% proteins from the brain protein data sets have longer half-lives in the tissue than in cell culture. Since most of the available protein data sets have longer half-lives in the tissue than in the cell-lines, we selected those proteins in the linear model. The rest of the proteins which have shorter half-lives in the tissue than in the cell-line, can be explained with this model as well, if we disregard those proteins which have half-lives greater than 200 hours in the cell. And we can do this since the authors[Once we remove proteins with half-lives greater than 200 h, only 6% of common liver proteins have longer half-lives in the cell-line. We classify 83% of these proteins by using the above described NN. These proteins also exhibit a linear relationship of half-lives  from the1) group as nucleic acid binding  belong in the oxidoreductase category Click here for additional data file.S2 Fig1 contains very long-living proteins while the other clusters  contain short-living proteins.The protein clusters are obtained from the linear regression line (the red line in the left plot). The cluster C(TIF)Click here for additional data file.S3 Fig1 contains very long-living proteins while the others  contain short-living proteins. The clustering improves the correlations between half-lives of proteins in the tissue and cell lines.The protein clusters are generated using the linear regression line (the red line in the left plot). C(TIF)Click here for additional data file.S4 FigThe half-lives of the uncommon proteins were predicted using a multivariate linear model and optimized weight vectors for each of the three clusters. For each protein its cluster was determined using the NN.(EMF)Click here for additional data file.S5 FigScatter plot of common liver proteins that have longer half-lives in the cell-line data . These proteins exhibited a linear relationship of half-lives which we could fit with our model.(EMF)Click here for additional data file.S6 FigMost of the long-living proteins belong to the class of nucleic acid binding proteins.(TIF)Click here for additional data file.S7 Fig1 cluster proteins was the integrin signaling pathway.The most enriched pathway among the C(TIF)Click here for additional data file.S8 FigMost of the proteins from this cluster belong to the class of nucleic acid binding proteins.(TIF)Click here for additional data file.S9 Fig2 two pathways were enriched: 1) inflammation mediated by the chemokines and cytokines signaling pathway, and 2) the integrin signaling pathway.For the proteins of the cluster C(TIF)Click here for additional data file.S10 FigThe most enriched for the proteins of this cluster was the oxidoreductase category. Also enriched were the nucleic acid binding and the enzyme modulator proteins.(TIF)Click here for additional data file.S11 FigThe short-living proteins belonged to the ubiquitin-proteasome pathway.(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 TableGmRNA in C1,C2,C3.).Negatively-correlated protein properties  have 0 w(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.S8 TableNegatively-correlated protein half-life properties e.g.  have 0 w(DOCX)Click here for additional data file.S9 Table2C provides the best result. It has predicted 76% of protein half-lives within 10% deviation from the experimental value.(DOCX)Click here for additional data file.S10 Table(DOCX)Click here for additional data file.S11 Table(DOCX)Click here for additional data file.S12 Table(DOCX)Click here for additional data file.S13 TableNegatively-correlated protein half-life properties e.g.  have 0 w(DOCX)Click here for additional data file.S14 Table2C provides the best result. It has predicted 33% of protein half-lives within 10% deviation from the experimental value.(DOCX)Click here for additional data file.S15 Table(DOCX)Click here for additional data file.