Bacillus thuringiensis, a Gram-positive bacterium that synthesizes a protein that, when ingested by the pests, kills them and does not harm other species.For many years, the use of chemical agents to control crop pests has been degrading the environment, bringing problems to humans and all living things. An alternative to deal with the pests is the use of biopesticides, biological agents capable of controlling these harmful organisms. One kind of biopesticide is Bacillus thuringiensis and its proteins significance, this work presents a software tool, called CryGetter, that is capable of retrieving data related to these proteins, store it and present it in a user friendly manner. The tool also aims to align the protein sequences and generate reports containing some statistical data concerning the alignments that were made.Since the economical importance of Bacillus thuringiensis and its proteins to speed up their data retrieval and analysis, allowing them to generate more accurate results. In this sense, the tool circumvents the error prone task of manually getting all the necessary data and processing them in various software systems to get the same result as CryGetter gets in a unique semiautomatic environment.CryGetter was created to help researchers of  Bacillus thuringiensis (Bt) bacterium are a viable alternative for crop pest control using chemical pesticides [Bt have little effect on non-target insects and vertebrates like birds and mammals [Bt is a bacterium present in the soil and produces a protein called Crystal protein (Cry protein) during the sporulation phase. Such protein is lethal to various insect orders [Coleoptera, Lepidoptera and Diptera.The biopesticides produced by the sticides , 2 witho mammals –7. The Bt orders , includiTo date, about 600 genes that encode the Crystal protein from different isolates were identified . These gLepidoptera, at least four types of protein receptors are involved in the binding process of proteins to Brush Border Membrane Vesicles (BBMV), beginning the process of pore formation in different Lepidoptera larvae: a cadherin-like protein (CADR), a glycosylphosphatidyl-inositol (GPI)-anchored aminopeptidase-N (APN), a GPI-anchored alkaline phosphatase (ALP) and a 270kDa glycoconjugate [Bt are specific to certain insect orders, while others do not exhibit this specificity and can act on two or more different orders. The reason for this specificity is still unknown, but amino acid changes in this region have been associated with toxicity. Tiewsiri and Angsuthanasombat [242W244, 245F247, 248Y250 and 263F265) in Cry4B gene, that has a toxin that attacks insects of the Diptera order. This resulted in a decrease of toxicity to the mosquito Stegomyia aegypti. In recent decades, several plants of different species have been transformed with genes of Bt to be commercialized . The pattern recognition in the amino acid sequence of these proteins, that may be associated with specificity, could facilitate the use of these genes in the generation of new transgenic plants resistant to different crop pests, as well as the construction of Bt pyramided plants [Regarding onjugate . The pronasombat  made thed plants , 17, whiThere are many software tools capable of analyzing protein sequences like MEGA 6  , 19, MacIn order to support the study of Cry proteins, we created a specialized and open-source software tool that is capable of compile the data of Cry proteins in a central repository, allowing the retrieval of data related to each protein such as their primary structures, three-dimensional models (PDB files), related works, etc., allowing the manipulation of such data in alignment algorithms and generation of reports related to the alignments. This tool, called CryGetter, is presented and detailed in this work.Bacillus thuringiensis Toxin Nomenclature website [To implement CryGetter, we used the Java SE (Standard Edition) Platform, version 8  and othe website  and Entr website   file 1 of the BtNomenclature website  interface can be found in its documentation [entation .The result of the request to the Entrez service, in XML format, is processed by the tool, generating a set of temporary files that will later be used in the next step. To process these generated files, the tool needs to parse the returned XML and generate a object composition in memory. To do this, a class composition that reflects the XML structure was created and is shown in Fig. CryGetter extraction data file generation: The last step comprises the creation of a file that will be saved by the user .To open an extraction file, the user must click on the “Load” button highlighted in section A2 of Fig. When pressed, this button triggers the execution of the extraction module that executes four steps: As result, all Cry protein data is loaded into the tool and shown in the left list , divided into three domains (if these domains are present in the NCBI data) and a diagram showing the position of the domains inside the entire sequence. This tab is then divided in four tabs:Complete Protein: The complete amino acid sequence of the– protein : In this tab, the 3D models (if they exist) of the protein are listed  amino acid and Cry1Aa1 has a 205Y207 (Tyrosine) amino acid. The user can even generate a report that summarizes the alignment data. This feature is presented in the next section, where an experiment comprising the analysis of two different Cry proteins was conducted.In this interface, the user can analyze the alignment by choosing two different proteins and clicking on the “Analyse” button. Doing so, the program will identify the differences between the selected proteins and show them in the differences list. In the example shown in Fig. CryGetter is currently being used as a support tool for two works related to Cry proteins structure analysis. The tool is proving to be useful, since it can automatize the data extraction task and perform a sort of functions related to proteins, drastically reducing possible errors in manual data collection of online sources, since if such task was being done manually, the chance of human error would be enormous, because the process of copy/paste large quantities of data, for example, of protein sequences available through a NCBI web-page to a specific tool, would probably add undesirable and difficult to detect errors like, for example, data truncation, unless all sequences are constantly reviewed. In the next subsection we present a study case of the use of the tool.Cry1Aa1: this Cry protein affects mainly the Diptera order;Cry3Aa1: the Coleoptera order is affected by this type of Cry protein.For this study, two Cry proteins were chosen to be analyzed based in their order specificity: After loading a generated data package in CryGetter, the “Alignment” button : Microsoft Windows®; 7 or aboveProgramming language: JavaOther requirements: Java Runtime Environment (JRE) 8 [License: GNU General Public License v3.0Any restrictions to use by non-academics: None (JRE) 8  and Perl (JRE) 8  for MVie (JRE) 8 License:


MicroProteins are small single-domain proteins that act by engaging their targets into different, sometimes nonproductive protein complexes. In order to identify novel microProteins in any sequenced genome of interest, we have developed miPFinder, a program that identifies and classifies potential microProteins. In the past years, several microProteins have been discovered in plants where they are mainly involved in the regulation of development by fine-tuning transcription factor activities. The miPFinder algorithm identifies all up to date known plant microProteins and extends the microProtein concept beyond transcription factors to other protein families. Here, we reveal potential microProtein candidates in several plant and animal reference genomes. A large number of these microProteins are species-specific while others evolved early and are evolutionary highly conserved. Most known microProtein genes originated from large ancestral genes by gene duplication, mutation and subsequent degradation. Gene ontology analysis shows that putative microProtein ancestors are often located in the nucleus, and involved in DNA binding and formation of protein complexes. Additionally, microProtein candidates act in plant transcriptional regulation, signal transduction and anatomical structure development. MiPFinder is freely available to find microProteins in any genome and will aid in the identification of novel microProteins in plants and animals. Genomes of higher eukaryotic organisms encompass on an average roughly between 15,000 and 25,000 protein-coding genes. Processes such as alternative splicing, alternative promoter usage, alternative polyadenylation and, at the protein level, proteolytic processing, can significantly increase the number of protein variants these organisms can produce. Furthermore, the formation of higher order protein complexes increases the functional diversity of proteins. Such higher order protein complexes are often composed of multiple components. Many proteins also associate with different types of complexes in which they adopt varying roles. MicroProteins have the ability to interfere with larger proteins and hinder them from engaging in higher order protein complexes; they can also sequester their targets into other types of complexes thus providing novel activities. Taken together, microProteins are important and potent modulators of biological processes.trans-microProteins) and most of these transcription units evolved during the evolution of genomes where both whole-genome and local duplications and rearrangements resulted in an amplification of protein-coding sequences followed by a subsequent loss of functional domains (cis-microProteins). In either case, the microProtein is related to a larger protein with different functional domains and interferes with the function of these “precursor proteins”  protein INHIBITOR OF DNA-BINDING (ID). ID was identified almost three decades ago  as an inC. elegans, and 95 in Arabidopsis, 204 in tomato, 156 in potato, 94 in rice and 334 in maize. This new microProtein dataset provides a valuable resource for investigating mechanisms of microProtein functions in plants and animals and the miPFinder program can be used to analyze new genomes as soon they become available. As we outline below, miPFinder is tunable and therefore allows relaxation of the stringent setting to identify hidden microProteins for example where protein interactions have not yet been discovered.It is implausible that microProteins are more abundant in plant genomes when compared with animal genomes or that they exclusively target transcription factors. To identify a larger variety of potential microProteins and microProtein regulatory modules in plant and animal genomes, we have performed a computational approach taking protein size, domain organization, known protein interactions and evolutionary origin into account. This approach yielded in the most stringent setting the identification of 1,108 individual high probability microProtein candidates belonging to 482 protein families, with 90 in human, 54 in mouse, 22 in zebrafish, 23 in fruit flies, 36 in Incomplete protein sequences were identified and removed from each protein data set in order to enrich for complete coding sequences. For human and mouse, proteins encoded by the representative protein-coding “GENCODE Basic” transcript set were used. GENCODE combines manual and automatic annotation and aims to annotate all evidence-based gene features in human and mouse genomes at a high accuracy. GENCODE’s Transcript Support Level (TSL) highlights the well-supported and poorly supported transcript models, and transcripts without any transcriptional evidence (TSL5) were omitted. Because the GENCODE annotation is only available for mouse and human, another approach was chosen for the remaining datasets. To deplete incomplete sequences for other organisms, only peptides which were derived from protein-coding nucleotide sequences that contain a start codon (ATG), stop codon , and a length that is a multiple of three were considered.All microProteins known to date are small in size, ranging from 7 to 17 kDa, overall comprising less than 120 amino acids . To exerA second parameter to take into account when trying to identify novel microProtein candidates is the protein organization of potential targets or ancestor. As described earlier, trans-microProteins exist as individual transcription units allowing their evolutionary origin to be traced back. A good example are the plant-specific ZPR proteins that originate from a large homeodomain leucine-zipper ancestor molecule, which got sequentially shortened by gene duplication, degeneration, and truncation . The ZPRIn the first step, miPFinder assigns protein sequences as putative microProteins and putative ancestors solely by size . TherefoIn addition to the significance values , we created a rating system that favors known microProteins. This rating is based on the clustalw alignment of the microProtein candidate family and their putative ancestor(s). First, conserved regions  are aligned  and regions with low gap content (length ≥20aa and gaps ≤10%) are extracted. This step enriches for regions with high similarity and extracts potential domains. Next, each microProtein candidates and putative ancestors are extracted and two consensus sequences are assembled. The similarity of the consensus sequences is rated based on the Blosum62 table and the following equation:Here, the score is modified by the alignment length in proportion to the length of the microProtein candidate. The resulting alignment rating favors known microProteins and is inversely related to the e-values  online, MicroProtein function requires interaction with respective target proteins. MicroProtein-candidates containing known protein–protein interaction domains, or sequences related to PPI-domains are therefore more likely to function as microProteins compared with small proteins not containing such domains. To identify and annotate protein–protein interaction domains within microProteins and ancestral proteins, miPFinder utilizes the Pfam and iPfam databases.MiPFinder assigns Pfam domains to all large proteins , reports domains that have similarity to microProtein candidates (≥60% length of the Pfam domain) in order of significance, and matches these to interchain interaction domains in iPfam. Domains with interchain interaction properties mediate interactions between amino acid chains, a prerequisite for microProtein function.Additionally, Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) v10  protein-http://diseases.jensenlab.org/in December 2015  categories, GO terms for metazoan protein databases were obtained from ENSEMBL and Plant GO terms were retrieved from AgriGO v1.2 . FinallyProtein classes were assigned to the most significant putative ancestor of each microProtein candidate family using PANTHER v11  and collhttp://www.python.org) and tested for Windows 7. MiPFinder requires the standalone applications hmmer3 (http://hmmer.org/), clustalw2  (ftp://ftp.ncbi.nih.gov/) (https://github.com/DaStraub/miPFinder).The program is written in python v2.7.9   and BLASih.gov/) . These ahttp://www.ipfam.org/(ftp://ftp.ebi.ac.uk/pub/databases/Pfam/) (http://www.phytozome.net) (http://www.ensembl.org/biomart) .The interaction domain database iPfam v1.0, June 2013, was obtained from pfam.org/ and Pfams/Pfam/) . Plant some.net)   as well as all cutoff values.The miPFinder program takes a single command line in the windows command prompt (e.g. “python miPFinder.py -f proteins.fasta -p ProteinGeneList.tsv -a annotation.tsv”). The minimum input requirement is a simple fasta file with all protein sequences (“-f”), however a file with protein annotations (“-a”) will aid the microProtein selection tremendously. Protein-interaction information from STRING data can be added via “-S” and for the addition of protein–protein-interaction domain information, a Pfam domain database (“-d”) and a file specifying interaction domains (“-i”) is necessary. Moreover, a file specifying the protein–gene relationship (“-p”) will allow for MiPFinder is built with Python v2.7.9 running on Microsoft Windows 7, and using hmmer v3.1b1, BLAST+ v2.2.29, clustalw v2.1, but any python2, hmmer3, BLAST2, clustalw2 and Microsoft Windows version might be sufficient for running the program. Path to the dependencies  must be specified if the accessory programs are not set as environment variables, using command line arguments “-H”, “-B”, “-C”, respectively. MiPFinder will check the availability of specified input files and correct function of all dependencies before each run.All microProteins known to date are small in size and require only a single functional domain that acts as a protein-interaction platform to sequester their targets. MicroProtein targets are known to be significantly larger than their microProtein counterparts and contain multiple protein domains. The size differences of microProteins and their targets range from 2- to 10-fold, as exemplified by MIP1A/B microProteins and their 3 times larger target CONSTANS , and LITbona fide microProteins than those without such properties, and are therefore preferred.The core mechanism of microProtein function relies on their ability to interact with respective target proteins. Protein–protein interactions can be inferred from sequence similarity to an interaction domain or collected from public interaction databases such as STRING. Candidates with indication for interaction capabilities are more likely The miPFinder program takes all these considerations into account  and builMiPFinder was used to investigate several metazoan and plant genomes with the aim to identify novel microProteins and produce a list of high probability candidates. In most protein databases, sequences are derived from translated RNA transcripts, which in some cases represent only truncated versions of full-length mRNA sequences. In order to prevent these mRNA fragments from being identified as microProtein candidates, human and mouse transcripts without any transcriptional evidence were omitted. For other organisms, only peptides that were derived from transcripts containing a start codon, a stop codon and a length that is a multiple of three were considered. The percentage of sequences that passed the quality filter varied considerably. In most organisms, >98% protein sequences appeared to be complete, however in maize and zebrafish only 91% and 72% of the protein sequences passed the filter. Additionally, 60% of human and 72% of mouse transcripts and their corresponding proteins are in Ensembls GENCODE basic set, and of these, ∼80% are either with transcriptional evidence or not tested for expression .Table 1OFollowing the enrichment of full-length sequences, the respective datasets were analyzed with miPFinder. The resulting microProtein candidates are annotated with various information, such as whether they are alternative gene products, similar to an interaction domain, known to interact with one of their potential ancestors, and the size distribution of related sequences to allow filtering for specific features and to enrich for high probability candidates  online.cis-microProtein candidates, which are alternative products of their ancestor genes, make up the majority of microProteins identified in these species, although in potato and maize these numbers are considerably lower  and are part of protein families where the larger proteins represent the majority of the respective protein family. This set of high probability microProtein candidates was further used to validate the method and to identify novel microProteins.In order to validate and test our computational approach, we employed miPFinder on the Arabidopsis genome and found that 18 of the 22 known Arabidopsis microProteins  are presP value < 0.01) of all human high confidence microProtein candidates are disease-associated, and around one-third is associated with severe diseases such as cancer  is a high probability microProtein candidate that interacts with NLR family proteins that are part of inflammasome complexes and thereby disrupt inflammasome assembly . POP2 alPTK6 gene produces two splice variants, the 52-kDa full length PTK6 protein and a 15-kDa alternative splice product, named ALT-PTK6, which miPFinder discovered as potential microProtein. Even though ALT-PTK6 and full length PTK6 interaction is not detectable, ALT-PTK6 associates with PTK6 substrates and coexpression of both PTK6 and ALT-PTK6 negatively modulates PTK6 protein–protein associations, possibly by competitive binding (Protein tyrosine kinase 6 (PTK6), also called breast tumor kinase (BRK), promotes in disease oncogenic signaling possibly due to intracellular localization . The PTK binding .These two examples showcase the potential of miPFinder results and its implication in human health. Both examples seem to fit the microProtein mode of action and act at important hubs for human well-being.To this end, we identified high probability microProtein candidates that are known to interact with their potential targets and we describe one example of one of these top‐ranked candidates that can be linked to evidence that strongly points to microProtein function. The formation of protein complexes is a prerequisite for microProtein function and using the protein–protein interaction database STRING to define high probability microProtein candidates proved valuable on the one hand but is also very restrictive on the other. Using the STRING database to test the interaction between a microProtein candidate and its potential ancestor(s) disregards common interactions with a third protein, thus limiting detectable associations to only homotypic domain interactions. An alternative less restrictive approach is to filter for sequence conservation. Because proteins that are conserved in several related species are more likely to have retained a function under evolutionary pressure. Additionally, conserved sequences are less prone to be annotation artifacts or degenerated pseudo-genes. However, it is important to note, that species-specific microProteins should not be ignored because they could be involved in species-specific traits and in some cases might even have acted as facilitators of speciation.To assign evolutionary conservation to microProteins, individual microProtein candidates were combined with OrthoFinder  results.Individual microProtein candidates were tested for their presence in all 11 species that were examined in this study. The known microProteins ZPR, MIF, Myb-microProteins, and HLH-microProtein (KDR-ILI1-like subgroup) are identified in all plants, while the HLH-type microProteins (PAR subgroup) and MIP1A/MIP1B are only present in dicotyledonous plants.Sorghum bicolor to 2,055 in maize, are conserved in at least two plant species  of proteins are conserved exclusively among at least two of the five metazoan proteomes  contained at least one transcription factor (TF). TF Basic Domains Group (e.g. bZIP), Helix-turn-helix , Other Alpha-Helix Group and Zinc-Coordinating Group (e.g. zf-C2H2) have microProtein candidates in all investigated metazoans (C. elegans)  .Fig. 3.—Gene Ontology (GO) terms describe gene products in terms of their associated biological processes, cellular components and molecular functions in a species-independent manner. In order to visualize and summarize the function of microProtein candidates, the most significant ancestor of each microProtein candidate family was analyzed for its GO annotation. MicroProtein candidate families were divided into several subsets based on their conservation according to OrthoFinder to investigate their roles in different evolutionary backgrounds . AccordiPANTHER annotates proteins through evolutionary relationship with descriptive protein classes . AccordiWe have developed miPFinder, a program that both identifies and classifies microProteins, which are important regulators of protein function. MiPFinder starts with a set of protein sequences and considers information about protein size, sequence similarity, domain composition and protein interaction to create a list of microProtein candidates. Additionally, when combined with protein conservation information, miPFinder can discriminate between microProteins that occur in several species or microProteins that are species-specific. This resource will aid the identification of microProteins and will promote research on the function of novel microProteins.An earlier version of miPFinder identified the Arabidopsis microProteins MIP1A and MIP1B  that conBecause microProteins are under-investigated in animals compared with plants, we aimed to find microProtein candidates related to human health among miPFinder results. As our results suggest, microProteins might play important roles in disease development and mutations in microProtein encoding genes could deregulate vital systems like cell cycle regulation that might eventually lead to cancer. A large percentage of human high probability microProtein candidates are disease related further supporting this notion. A particularly interesting case is the alternative splice variant of breast tumor kinase (BRK or PTK6). The human PTK6 has strong indications for microProtein function, such as coexpression of full length PTK6 and the alternative product (ALT-PTK6) negatively modulated PTK6 protein–protein associations and ALT-PTK6 seemingly competed with the full-length protein for interaction partners . This coBecause microProteins act by engaging in direct protein–protein interactions, candidates with similarity to a known protein-interaction domain are more promising than those without any known domains. MiPFinder annotates protein domains to a given set of sequences, but already existing domain information can also be provided if desired. However, some proteins interact via discontinuous sequences that form three-dimensional interaction interphases rather than with specific interaction domains. Databases such as STRING contain known protein–protein interactions independent of domain annotations and infer these to evolutionary conserved proteins in different species, even so, only a fraction of in vivo interactions might be captured. Due to these constraints, miPFinder does not filter for interaction abilities it simply annotates potential common interaction domains and known protein–protein interactions of microProteins and their related larger target proteins. Thus highest priority can be given to microProtein candidates with known interactions or interaction abilities but the search also includes all other candidates.trans-microProteins) as seen in the case of cis-microProteins which are splice variants of larger proteins. The human cis-microProtein of Regulator of G-protein signaling 5 (RGS5), a small splice variant that can negatively inhibit its targets function , was initially identified in mice  and miPFfunction , is not Focusing solely on microProtein candidates with annotated protein–protein interactions with their putative ancestors proved valuable in finding high confidence microProteins but was also very restrictive enriching for well-studied proteins. An alternative approach considers conservation information in order to enrich for proteins with function under evolutionary pressure. This approach yields much more diverse microProtein candidates but lacks the confidence for protein–protein interaction.Several known Arabidopsis microProteins can be found in either all of the six plant genomes that we have investigated here or in at least in one of the subsets of the three dicot or monocot genomes. MicroProtein candidates that are conserved among all investigated species  seem less likely to have microProtein function because related sequences of these proteins are overall relatively small and larger protein sequences are only distantly similar. In general, we find that microProteins that are conserved in at least a few other species have an increased probability that the small, often one exon sized microProtein candidates are not pseudogenes. Consequently, microProtein candidate families that are conserved in several but not all of the 11 genomes are promising candidates. Good examples are the LITTLE ZIPPER microProteins, which regulate leaf development and that are conserved in the whole plant euphyllophyte clade, and MIP1A/B, which have been shown to fine-tune flowering of Arabidopsis, which are conserved in all dicotyledonous plants.To learn more about the biological processes both microProteins and their putative targets are involved in, we categorized the most significant ancestor of each microProtein candidate family into functional groups and performed a Gene Ontology (GO) analysis. MiPFinder results showed high percentage of GO terms that are also found among ancestors of known microProteins such as “signal transduction” and “anatomical structure development” including several microProtein candidates that are related to transcription factors. This underlines the importance of microProteins in mediating responses to the environment and basic patterning pathways, which are exemplified in the role of known microProteins, such as ZPRs in Arabidopsis leaf development.Since known plant microProteins are involved in regulation of transcription, we compared our metazoan miPFinder results to a transcription factor database. Putative microProteins are present in several major transcription factor families in all studied metazoan genomes. Analysis for microProtein regulated protein classes revealed that transcription factors are a sizeable fraction of microProtein targets in human but only the exception in plants. This implies that regulation of protein activity by microProteins extends beyond the regulation of transcription factors and affects to a large extend other protein classes in plants.The identification and experimental characterization of novel microProteins, based on miPFinder, will allow further improvement of the program. Knowledge of more microProteins will aid in refining the parameters in order to further improve the list of microProtein candidates. Additionally, future upgrades of the source databases will benefit microProtein identification. Most importantly, a complete and accurate annotation of all small transcripts and respective protein sequences including splice variants will allow for better microProtein detection.In summary, selecting microProteins from miPFinder for experimental validation is ideally guided by taking all the above-mentioned criteria into account. For example, MIP1A- and MIP1B-related protein sequences are in majority large in size (65%), the relative fraction of microProteins is small (6%), and the sequence similarity is rated high. Additionally, MIP1A and MIP1B resemble an annotated protein–protein interaction domain (B-box) and MIP1A is annotated by STRING to interact with several large related proteins. They are also exclusively conserved among all three investigated dicots . Therefore these candidates fit perfectly into the scheme of microProteins and were experimentally confirmed to have microProtein function . Howeverhttps://github.com/DaStraub/miPFinder. Taken together, miPFinder allows the rapid identification of novel microProtein regulators and can be applied to any close-to-complete genome. All settings are adjustable thus allowing users to perform a variety of searches according to their needs. Up to date, microProteins are underinvestigated in animals compared with plants and miPFinder enables the identification of microProteins in all available genomes. The miPFinder algorithm is freely available under the GPLv3 license at Genome Biology and Evolution online.Supplementary DataClick here for additional data file.


Structural excursions of a protein at equilibrium are key to biomolecular recognition and function modulation. Protein modeling research is driven by the need to aid wet laboratories in characterizing equilibrium protein dynamics. In principle, structural excursions of a protein can be directly observed via simulation of its dynamics, but the disparate temporal scales involved in such excursions make this approach computationally impractical. On the other hand, an informative representation of the structure space available to a protein at equilibrium can be obtained efficiently via stochastic optimization, but this approach does not directly yield information on equilibrium dynamics.We present here a novel methodology that first builds a multi-dimensional map of the energy landscape that underlies the structure space of a given protein and then queries the computed map for energetically-feasible excursions between structures of interest. An evolutionary algorithm builds such maps with a practical computational budget. Graphical techniques analyze a computed multi-dimensional map and expose interesting features of an energy landscape, such as basins and barriers. A path searching algorithm then queries a nearest-neighbor graph representation of a computed map for energetically-feasible basin-to-basin excursions.Evaluation is conducted on intrinsically-dynamic proteins of importance in human biology and disease. Visual statistical analysis of the maps of energy landscapes computed by the proposed methodology reveals features already captured in the wet laboratory, as well as new features indicative of interesting, unknown thermodynamically-stable and semi-stable regions of the equilibrium structure space. Comparison of maps and structural excursions computed by the proposed methodology on sequence variants of a protein sheds light on the role of equilibrium structure and dynamics in the sequence-function relationship.Applications show that the proposed methodology is effective at locating basins in complex energy landscapes and computing basin-basin excursions of a protein with a practical computational budget. While the actual temporal scales spanned by a structural excursion cannot be directly obtained due to the foregoing of simulation of dynamics, hypotheses can be formulated regarding the impact of sequence mutations on protein function. These hypotheses are valuable in instigating further research in wet laboratories.The online version of this article (doi:10.1186/s12864-016-2798-8) contains supplementary material, which is available to authorized users. Experimental, theoretical, and computational studies have shown that protein function is the result of a complex yet precise relationship between protein structure and dynamics –3. Long While significant advances have been made in the wet laboratory , 9–12, eComputational methods that simulate the constrained dynamics of the bonded atomic particles in a protein molecule via iterative application of Newton’s laws of motions are appealing. By following motions of atoms along the negative gradient of a molecular mechanics force field, these methods, also known as Molecular Dynamics (MD) methods, directly simulate structural excursions of a protein . Since eThe challenges regarding characterization of equilibrium protein dynamics are better understood from a protein energy landscape perspective, which links protein structure, dynamics, and function . BrieflyThe energy landscape view clarifies why a complete and detailed account of protein equilibrium dynamics is a non-trivial task. In principle, the task requires a comprehensive characterization of both the protein structure space and the underlying energy landscape that governs the accessibility of structures and excursions between them at equilibrium. While wet-laboratory studies may not catch semi-stable structural states (due to insufficiently long dwell times), computational approaches that simulate protein dynamics quickly become computationally intractable for even moderate-size proteins.First, stochastic optimization (randomized/stochastic search) is employed to explore a protein’s structure space and construct a map of the energy landscape relevant for equilibrium dynamics. Second, the map is analyzed and queried for paths of intermediate structures that link two structural states of interest, effectively yielding an ensemble of paths that provide an on-demand view of the equilibrium dynamics relevant to a specific structural excursion. This two-step approach foregoes any direct information on the temporal scales involved, as there is no notion of physical time in the computed structures and paths connecting them. However, by doing so, the computational demands become much more reasonable; for instance, investigations of medium-size proteins of 150 or more amino acids can be conducted on small clusters (of no more than 16 CPUs) in no more than a few days (ranging from 7 to 15). Moreover, during this process, close to a million structures are generated, embedded in multi-dimensional maps of energy landscapes, and available to answer queries about energetically-feasible structural excursions at equilibrium between any two structures of interest.In this paper we present a novel computational methodology that takes a complementary approach to the MD-based approach. While the goal remains to elucidate the equilibrium dynamics of a protein by computing structural excursions at equilibrium, the dynamics are not simulated directly. Instead, a two-step approach is followed. de novo protein structure prediction, EAs with domain-specific insight have been shown to rapidly locate the global minimum and reproduce the native structure , where s is a user parameter. The magnitudes of the movements along the other PCs respect their variance relative to the variance captured by the first/top PC. While the shape of the space is preserved, the boundaries of the m-dimensional embedding of the wet-laboratory traces are not observed, as the ultimate goal is to generate individuals that represent new structures not captured in the wet laboratory for the target sequence α.As line 6 in Algorithm 2 indicates, each parent etail in , 28. In c is subjected to an improvement operator to obtain a better offspring The obtained offspring m-dimensional PC space, is converted into a set of backbone atoms with coordinates in 3D. This step consists of recovering the CA trace via simple algebra operations (detailed in  view. This basin sits at the top of the map, in between the On and Off basins, and is referred to as the Conf2 basin. Conf2 is not populated by the lowest-energy structures, but it does contain low-energy structures and two experimentally-known ones. The latter are reported in the PDB under ids 1Q21 and 2Q21. The structure with PDB id 1Q21 is reported as active/On for the WT, whereas that with PDB id 2Q21 is reported as active/On for the G12V variant . The strThe comparison of landscapes and path ensembles across the H-Ras variants provides observations that not only validate and reconcile wet-laboratory findings but may also be useful to further investigation in the wet-laboratory on understanding mutations and designing inhibitors to disrupt aberrant activity . For insThis paper introduces a novel methodology to map a protein’s energy landscape and model equilibrium dynamics. Rather than simulate the dynamics of the covalently-bound network of atoms in a protein molecule, the proposed methodology relies on stochastic search to obtain a sample-based representation of the constrained structure space relevant for the dynamics, and then employs discrete search structures to summarize the dynamics. An EA is employed to map the multi-dimensional energy landscape of a protein, and a nearest-neighbor graph representation of the map is then queried to reveal energetically-feasible successions of structures mediating structural excursions of interest. Analysis of applications on several proteins of importance to human biology and disease suggests the proposed methodology is useful in understanding the relationship between protein structure, dynamics, and function with a practical computational budget.While obtaining a detailed characterization of protein equilibrium dynamics remains a challenge in silico, the work here exploits the wealth of structure data and novel randomized search strategies to enhance exploration of the thermodynamically-available structure space. The exploitation of structure data is a powerful and timely mechanism to map the structure space of a protein. The availability of wet-laboratory structures representing semi-stable and stable structural states for many proteins allows formulating algorithms that can map energy landscapes within a reasonable computational budget, as demonstrated here.The work presented here opens up several promising directions for future research. One direction concerns lowering the dependency of the methodology on sufficient structure data, as well as expanding its applicability to systems where experimentally-known structure reside in a non-linear low-dimensional space. The first can be addressed via techniques such as Normal Mode Analysis, already integrated with some success in robotics-inspired modeling of protein motions –70. The Another direction of future research concerns improving the predictions of the locations and depths of mapped basins by employing various energy functions. This direction aims to increase the reliability of in-silico predictions. Considering multiple energy functions remains challenging, however, as considerable recoding efforts are required to efficiently integrate such functions in in-house code.All data obtained by the proposed methodology and analyzed here are available to the research community upon request. Similarly, any components of the proposed methodology can be shared as linux binaries.


Protein–protein interactions dominate all major biological processes in living cells. We have developed a new Monte Carlo-based simulation algorithm to study the kinetic process of protein association. We tested our method on a previously used large benchmark set of 49 protein complexes. The predicted rate was overestimated in the benchmark test compared to the experimental results for a group of protein complexes. We hypothesized that this resulted from molecular flexibility at the interface regions of the interacting proteins. After applying a machine learning algorithm with input variables that accounted for both the conformational flexibility and the energetic factor of binding, we successfully identified most of the protein complexes with overestimated association rates and improved our final prediction by using a cross-validation test. This method was then applied to a new independent test set and resulted in a similar prediction accuracy to that obtained using the training set. It has been thought that diffusion-limited protein association is dominated by long-range interactions. Our results provide strong evidence that the conformational flexibility also plays an important role in regulating protein association. Our studies provide new insights into the mechanism of protein association and offer a computationally efficient tool for predicting its rate. Kd) that quantitatively determine the stability of a protein complex after bindingKd, the kinetic aspect of binding  is usually as important to the biological functions of proteins in cells12kon, and the rate of dissociation, koff, is defined by Kd = koff/kon, in which kon and koff have units of M−1 s−1 and s−1, respectively, if a first-order reaction is considered in which one ligand only binds to one receptor. The values of kon and koff can be experimentally measured using a number of in vitro and in vivo methods. In vitro biophysical techniques, such as analytical ultracentrifugation (AUC)18in vivo approaches such as cross-linkingkon span an extremely wide range: between 1 M−1 s−1 and 1010 M−1 s−1 262728293031konProtein interactions constitute an indispensable part of all cellular processes1234578in vitro experiments and thus cannot definitively represent the in vivo binding of proteins.A number of different models for the mechanism of protein association have been proposed. The earliest proposed mechanism was the lock-and-key model, in which binding was described as rigid-body docking with surface complementarity. In an alternative approach, known as the induced-fit model3537394041424344454647484950515253545556575859616263kon values of which range from 104 to 109 M−1 s−1. Positive correlations were observed between the experimental measurements and our calculated values, indicating the potential of the method for predicting the rate of protein association. However, the kon values for some of the protein complexes were overestimated in the benchmark test. Based on the conformational selection model, we hypothesized that this overestimation resulted partially from the molecular flexibility at the interface regions of the interacting proteins. After inputting variables, including the percentage of flexible loop residues from each protein at the binding surfaces to take into account the impact of the molecular flexibility using a machine learning algorithm, we successfully distinguished the most overestimated association rates from the non-overestimated ones and were thus able to correct the overestimated rate constants and improve the final prediction in a cross-validation test set. This method, which, to the best of our knowledge, is the first to combine physics-based simulation and a machine-learning algorithm, was then applied to a newly constructed independent 10 complex test set, and a strong correlation was obtained between our predicted kon values and the experimentally measured values. It was thought that the protein association in a diffusion-limited system is dominated by long-range interactions at the binding interfacesThe usage of coarse-grained (CG) models is an alternative strategy that enables higher computational efficiency by reducing the size of the simulation system. CG models have been developed to study protein–peptide and protein–protein binding and complex assembly6566264 simulation trajectories with a maximal duration of 1000 ns were generated, and encounter complexes were observed in 658 of these, giving a success rate, ρ, of 0.0658. Knowing the volume of the simulation box, the maximal duration of each trajectory, and the success rate, kon, which was 4.22 × 107 M−1 s−1. This result, which is close to the experimental measurement (1.2 × 108 M−1 s−1) at the ionic strength of 103 mM, indicates a fast association between these two proteins.We used the association of the proteins barnase and barstar as a test system to evaluate the robustness of our KMC simulation. The barnase/barstar complex (PDB id 1BRS) was separated into two monomers and randomly placed in a 10 × 10 × 10 nm cubic simulation box. The parameter ξ (Coulomb Debye length) in the simulation was 9.5 Å, which corresponded to an ionic strength of 103 mM. The relation between ξ and the ionic strength will be discussed in the next section. Starting from a random orientation of two monomers, 10kon cannot be derived without the statistical analysis of all 104 trajectories.Three representative trajectories are selected to illustrate the physical process of association in simulations. The changes of distance between two monomers’ centers of mass are plotted in kon values, shown by the red bars in kon was normalized by the maximal duration of the simulation and is thus temporally insensitive. We also tried simulation boxes of different sizes. As shown in kon values. Overall, the calculated kon values were relatively stable in large volumes, which suggested that the size of the simulation box had little effect on our prediction results under relatively low concentrations. However, the kon for a small-volume box was relatively low because the increasing nonspecific interactions at a high concentration hinder the proper association between two proteins. This effect is not considered in traditional simulation methods, in which the concentrations of the interacting proteins are essentially ignored. Our results suggest that in a crowded cellular environment, the protein association is concentration-dependent. This is consistent with previous studieskon of protein association.We then changed the maximal duration of each simulation trajectory. As shown in kon values for protein binding at different ionic strengths show a negative correlation69The concentration of ions around two interacting proteins is an important factor controlling the rate of their association, and the experimentally measured kB is the Boltzmann constant, e the elementary charge, I the ionic strength, T the absolute temperature, and ε the solvent medium dielectric constant. In our study, kon values plotted against the ionic strength are shown in kon values were underestimated compared to the experimental measurements, there was qualitative agreement between these two sets of data within the ionic strength range of 50 to 500 mM. The figure shows a fast association at low ionic strength and a slow association at high ionic strength, consistent with previous resultswhere kon values for these complexes have been reported in a previous studykon values for these mutants (striped bars) and the experimental values (gray bars) at an ion concentration of 50 mMkon values (higher than that for the wild-type complex), while K27D35 and R59 had the lowest.To systematically validate the sensitivity of our simulation algorithm and scoring function, we tested the effect of protein mutations on the calculated association rates. Mutations of specific residues at binding interfaces change the interactions between proteins, affecting their association rates. Our test set consisted of the wild type barnase/barstar protein complex plus 11 complexes of barnase mutants and wild-type barstar or complexes of mutants of both proteins in which the target amino acid(s) were mutated into alanine; the experimental kon values were underestimated compared to the experimental values. However, our computational model was able to reproduce the relative order of the rate constants for the mutant complexes compared to that for the wild-type complex. For instance, our simulations showed that the mutation of D54, E60, or E73 to alanine accelerated the association, while the mutation of K27D35 or R59 to alanine decreased the association. This result therefore suggests that for the barnase/barstar complex, our model can capture the effects of single- and double-point mutations on the association rates.kon and ionic strength have been reported previously4 simulation trajectories were carried out based on the reported ionic strengths and native structures listed in kon values were calculated based on the simulations for all the 49 complexes except two (3BP8 and 1VFB), for which the simulations did not generate any output. This could be because a multistep association mechanism was involved. To test the generality of our KMC simulation method, we used a large-scale benchmark set of 49 protein complexes for which experimental measurements of the Q, which is defined as the number of intermolecular native contacts formed during the simulations divided by the total number of contacts that would be found in the final complex. These criteria were normally varied in different studies, or even in the same study, to achieve the best agreement with the experimental results12TransComp, which is based on the “transient-complex” theory616263TransComp is calculated as Gele is the electrostatic interaction free energy of the transient complex. Comparing the kon values predicted by TransComp with those calculated from our KMC simulations, we observed that, in some cases, the kon values calculated by our model are closer to the experimental values than those predicted by TransComp. For example, for the complex CheY/CheA , our calculated value (5.8 × 107 M−1 s−1) is more accurate than that of TransComp (9.0 × 106 M−1 s−1). In some cases, on the other hand, kon values calculated by our model are less accurate than those of TransComp. For instance, the experimental kon of the complex Mlc transcription regulator/EIICB (PDB id 3BP8) is 1.0 × 106 M−1 s−1, which is closer to that of TransComp (6.3 × 106 M−1 s−1) than that of our simulation (3.0 × 104 M−1 s−1). The underestimation by KMC for this specific system is possibly due to the small binding interface in this complex. Starting from a random initial configuration, the native-like structure of this complex is relatively difficult to be sampled by the KMC simulation. It is also worth mentioning that the initial TransComp runs were not able to generate results for a few cases that contain extended interfaces in the native complexes, such as streptokinase/plasmin (PDB id 1BML) and thrombin/hirudin (PDB id 4HTC). To compare with the experiments in these cases, partial structures of the protein complexes were chosen as inputs. Our KMC simulations were able to produce reasonably accurate kon values for these cases. Moreover, we carried out a blinder benchmark test in which the full-length proteins of all 49 complexes were used in our CG simulations. As shown in the next section, the correlation with experimental measurements could be increased by integrating a machine learning-based module to identify potentially overestimated calculated results. Future upgrades of our Kinetic Monte-Carlo simulation are also proposed in the Discussion.Most previous studies of all-atom BD simulations were tested only on a few individual cases. The reaction criteria in these all-atom simulations are determined by a structural parameter, kon values calculated from the KMC simulations were overestimated for a large percentage of protein complexes in the benchmark set. Interestingly, we are not the first to observe this phenomenon. Gabdoulline and Wadekon values for three protein complexes were accurately reproduced, while those for the other two protein complexes were significantly overestimated by approximately 30-fold. The authors proposed that this may have been due to protein flexibility, suggesting that the flexibility of the secondary structure of the proteins at a binding interface may be related to the kon overestimation in many simulation models. Because the intramolecular degrees of freedom were fixed in our KMC simulations, the structural flexibility is also one of the factors that we did not take into account in the calculation of kon. This implies that the lack of intramolecular flexibility in the simulation might be one of reasons that led to the overestimation of the calculated kon values. Other factors, such as the electrostatic interactions between residues that are not at binding interfaces, could also cause non-specific interactions that interfere with the association rates. If we were able to identify the structural flexibility and other factors that are responsible for the overestimation and use them to identify protein complexes with overestimated kon values, we would be able to not only improve predictions purely based on KMC simulations but also better understand the molecular mechanisms of protein association.One major factor affecting the correlation with the experimental measurements was that the kon have values that are closer to the experimental values. Especially in the case of 1GXD, for which the kon was overestimated, we show that the conformational fluctuation due to the high structural flexibility can impede association. Unfortunately, ENM has difficulty in modeling large conformational changes due to its limitation of using the harmonic approximation of the force field. Its application to large-scale benchmark tests is under development. No other physics-based method is currently sophisticated enough to fully model the conformational flexibility in simulating protein–protein interactions. Previous methods only considered the effects of conformational changes indirectly by judicially selecting fragments of proteins in a complex as the input structures of simulations.Based on the results from our KMC simulations and similar observations in the study by Gabdoulline and Wade, we hypothesized that even when the protein association is dominated by diffusion, it is regulated by a combination of structural factors  and energetic factors (mainly electrostatic interactions). To validate the hypothesis, we applied a proof-of-concept analysis by integrating the elastic network model (ENM)71kon values. As described in the Methods, we introduced three indicators as inputs for the model. Two of these, the percentages of interface residues on the flexible loops of each of the two interacting proteins, account for the conformational flexibility, whereas the third, the ratio of the electrostatic potential at the binding interface to that of the whole protein pair, accounts for the energetic factor of association, particularly the non-specific interactions. A cross-validation test was then performed on the 47-protein complex benchmark set in which the leave-one-out strategy was applied to avoid potential over-fitting.Therefore, we decided to incorporate the structural flexibility by a different strategy. We added a machine learning-based module to identify and adjust overestimated kon values for 39 of the 47 complexes were predicted correctly as either overestimated or non-estimated, giving an accuracy of 83%. Moreover, of the 23 overestimated cases, 19 were successfully identified, giving a sensitivity of 82.6%. The detailed classification results are shown in kon values were adjusted by a corresponding correction factor based on the classification results; the detailed procedure is described in the Model and Methods. The black circles in kon values and the experimental values for all 47 complexes. In this plot, the Pearson’s correlation coefficient was increased to 0.79 from the original value of 0.66. This improvement resulted from the implementation of the machine learning-based module and highlights the importance of molecular flexibility. We have further performed the linear regression to the dataset. Specifically, the dashed red line in 10kon values, while the solid red line is from linear regression fit between adjusted and observed log10kon values. Considering the slope of 1 and intercept of 0 in a perfect correlation, the increase of slope and decrease of intercept indicate the prediction results have been improved after the application of machine learning. Thus, the new method combining physics-based simulation with machine learning not only enhanced the predictive potential of our model but also emphasized the functional role of conformational fluctuations, which has been underestimated in the diffusion-limited protein association class.The KMC simulations for the 47 complexes in the benchmark set resulted in 23 overestimations and 24 non-overestimations (no outputs for two complexes). Using the leave-one-out training and testing process, we found that the kon calculated from the simulations were compared to the experimental data, as shown in kon values was 0.8, and this strong correlation indicates the robustness of our KMC method in simulating the rates of protein association. The machine learning process was then applied to the same dataset to identify the potential overestimation in simulations. All 47 of the protein complexes in the previous benchmark set were used as training sets, and each of the 10 protein complexes in the new dataset was individually tested using the trained model. The three indicators for the corresponding protein complex were then input to predict the potential overestimation and the kon adjusted by the corresponding correction factor. Among the 10 complexes, there are 3 overestimated and 7 non-overestimated kons. After our training and testing process, we found that the values for 6 kons from the 10 complexes were predicted correctly as either overestimated or non-estimated, giving an accuracy of 60%. Moreover, of the 3 overestimated cases, 2 were successfully identified, giving a sensitivity of 66.6%. After the adjustment from machine learning, the final predicted results are plotted in kon values was 0.85. We have also performed the linear regression to this dataset, both before and after the application of machine learning. Specifically, the red line in 10kon values gave a slope of 0.68 and intercept of 2.18. The red line in 10kon values led to a slope of 1.25 and intercept of −1.98. This suggests that the correlation between predicted and experimental results after machine learning is not significantly improved, but rather over-rectified. The over-rectification is caused by the reason that one data point (3hfm) was misidentified, which significantly shifted the regression result. Moreover, in the dataset with relatively small size, individual cases can cause larger effect on the overall statistical result. Therefore, our study suggest that, although the correlation coefficient after machine learning (0.85) is better than before (0.8), the regression results indicate that a higher correlation coefficient does not necessarily lead to the improvement of prediction results.To further test the stability of our KMC simulations and rule out the possibility of model over-fitting during machine learning, an independent test set of 10 complexes was collected; the detailed information for this set can be found in the Methods and kon values. When the same dataset was tested by the TransComp server, the Pearson’s correlation coefficient with the observed kon values is 0.75. Taken together, our results demonstrate the stability of our computational method for predicting protein association rates and that there is no over-fitting in the training of the machine learning process.Finally, the overall data that combine the 10 complexes with the 47 complexes give a Pearson’s correlation coefficient of 0.78 between the logarithmic values of our predicted and observed kon that were consistent with the experimentally derived values under different simulation conditions, indicating the robustness of our method. Furthermore, after constructing a computational framework that integrated the KMC simulations into a machine learning algorithm, we obtained strong positive correlations between the experimental and predicted kon values for both a previously used benchmark set of 49 complexes and a newly constructed test set of 10 complexes, indicating the potential of our method as a powerful tool for predicting the in vitro protein association rates.Each cell contains millions of different proteins, the interactions of which maintain the routine functions of the cell737412ab initio. It has been proposed that the wide spectrum of protein association rate constants can be divided into two groups4 M−1 s−1, in which the association is limited by protein diffusion, and those lower than this value, in which the association is limited by conformational changes during binding. The significance of the electrostatic complementarity between two binding partners in allowing a fast association in a diffusion-limited system has been previously emphasized. In this diffusion-limited case, the proteins are normally modeled as rigid bodies in the simulation to calculate the association rates. However, in our model, when the conformational flexibility was not considered, we found that a group of protein complexes in the diffusion-limited class had computationally overestimated rate constants. This result is consistent with those of a previous studyOur computational prediction is based on a physics-based scoring function and Monte Carlo movements to accurately simulate the protein diffusion and conformational changes. This CG model therefore attempts to mimic the biological process of protein association It has been well accepted that machine learning algorithms are able to provide a mechanistic understanding to biological systems in addition to improving prediction results by adjusting multiple parameters. In terms of protein–protein binding, for example, a feature selection and regression algorithm was applied in a recent study to mine a large set of molecular descriptors about binding interfaces between proteinsIn our machine learning process, a standard benchmark set containing 49 protein complexes was used for machine learning. We believe that the sample size is large enough for learning in this model. This is due to the following reasons. Firstly, there are only three inputs in our machine learning: the percentage of interface residues on loops of each of the two interacting proteins (factor of flexibility) and the ratio of the electrostatic potential at the binding interface to that of the whole protein pair (factor of energetics). Moreover, the factors of flexibility and energetics are complementary with each other. There is no degeneracy in the space of inputs. At the meanwhile, there are not many other adjustable parameters in the algorithm of the “complex decision tree”. The only parameter is the criterion of overestimation, which has been used based on a previous studykon. The sensitivity of these factors to simulations needs to be evaluated on a systematic level.Despite the above-mentioned merits, our method has a number of limitations and can be further improved. First, the energy function in our model might be oversimplified, as it only takes into account the most dominant elements in protein–protein interactions. Some minor effects, such as short-range hydrogen bond interactions and electric dipole moments, can also play subtle roles in regulating the binding kinetics, and the improvement of our method will depend on how these factors are incorporated into the CG model. Another factor potentially affecting the accuracy of our method is the criteria used to determine the formation of an encounter complex. In our present model, the same criteria were applied to all protein complexes. However, as indicated in the transient-complex model, each protein complex has a unique binding interface and energy landscape, meaning that the criteria for the formation of different encounter complexes should be individually determinedkon of a protein complex is overestimated or not. The most commonly used learning rules that are used to partition decision trees are based on the maximum entropy or largest information gain. After the tree construction, new data points can be traversed through the tree from the root to one of the leaf nodes, from which the class of each data point can be determined. We used the software MATLAB to automatically construct and optimize the tree after providing types of input indicators and training datasets. Consequently, the details of the learning procedure were not revealed, and the criteria of classification cannot be fully understood. The future application of machine learning programs that require more manual involvement is therefore needed to understand the mechanistic details of the overestimation.Another issue rises from the use of the decision tree method as a “black box”. A decision tree is a series of Boolean tests that serve to classify the data. The structure of a decision tree consists of a root node, a set of internal nodes, branches and leaves. The classification algorithm starts from the construction of the tree, in which one of the input indicators is selected as the root node and the training set is divided into two or more subsets. Additional partitions are carried out by generating new internal nodes. The branches coming out of the root and internal nodes are labeled with possible values of the indicators, while the leaves correspond to a decision, in this case, whether the Nevertheless, the use of reduced representations enables us to tackle the problems of protein–protein interactions with spatiotemporal ranges that are beyond the accessibility of the all-atom model. Therefore, our CG model can be applied to biological systems that are difficult to study using previous methods. For example, we will be able to study the binding kinetics of proteins with domains separated by flexible loopskon and Kd are reflected by the surface density of the interacting molecules and are expressed in terms of μm2/s and molecules/μm2, while the units of the 3D kon and Kd are reflected by volumetric concentrations and are expressed by M−1 s−1 and M, if the first-order reaction is considered in which one ligand binds to only one receptor. This difference in units makes it very difficult to directly compare the 2D binding with the 3D. In one of our previous studies, we applied a similar method of coarse-grained kinetic Monte-Carlo simulation to study the binding of membrane receptors on cell surfaces. Using the interaction between the membrane proteins CD2 and CD58, two cell adhesion molecules known to mediate the activation of T cells and natural killer cells, as a test system, we showed that the 3D and 2D association rates could be directly linked and quantitatively compared8182We can also extend our model to study the interaction between membrane proteins. Compared with the soluble proteins, it is technically much more difficult to simulate the association of membrane proteins due to the complexity of membrane environments. Furthermore, the binding of membrane proteins (2D) is measured in units that are different from those for the binding of soluble proteins (3D)kon values. It is worth mentioning that there are other possibilities that might also cause the overestimation. For instance, the calculation of the rate constant might be affected by omitting the degrees of freedom in the coarse-grained representation of the protein, as described in a previous studykoff might potentially lead to the systematic overestimation of kon. However, the simulation of protein-protein dissociation is an extremely challenging topic, especially when the protein complexes are energetically stable. It will take very large computational resources to obtain the process of dissociation for a protein complex with a long life span. Fortunately, a multi-scaling modeling framework can be used to estimate both kon and koff in simulationFinally, we emphasized the importance of the protein conformational flexibility in regulating the protein association by assuming that the lack of conformational flexibility in the KMC is one of the reasons that led to the overestimation of calculated et al.4 to 1.3 × 109 M−1 s−1. Detailed information for this benchmark set is listed in In this study, two sets of protein complexes were used to test the KMC simulations and to train and test the machine learning-based classification model. First, we used a set of 49 protein complex structures collected by Qin To avoid over-fitting during the machine learning of the 49 protein complex structures in the benchmark set, we collected another independent test set of 10 protein complexes from the SKEMPI databaseα atom, while the other, indicated as S, is the representative center of a side-chain selected based on the specific properties of a given amino acid  described by a simple physics-based potential function consisting of three terms that can be written asThe total energy of interaction between two proteins during association . The charge was assigned to the representative center of the side-chain of each corresponding residue. ε0 is the vacuum electric permittivity. An effective dielectric coefficient, Deff = Ds exp(rij/ξ), is used to reflect the shielding effect between two residues in which the representative centers of the side-chain are separated by a distance of rij. As described in a previous studyDs = 10 is used to describe the local dielectric environment in which two proteins form an interface, and ξ is the Coulomb Debye length used to mimic the screening effect at different ion strengths, as discussed in the Results. The profiles of the electrostatic potential at different ionic strengths are plotted in 92The Kim-Hummer potential, together with an intramolecular Go-like potential, was developed to model flexible protein interactionshp, is the hydrophobic interaction, which is calculated by summing the hydrophobic scores of all contact residue pairs (residue i in chain 1 and residue j in chain 2) in which the representative centers of the side-chain are close to each other (rij < 6 Å)The second component, EHPi and HPj, were taken from a previous study by Kyte and Doolittlehp, which is used to re-scale the weights of the energy terms and determine the relative contributions between the hydrophobic and electrostatic interactions, is 0.04.The hydrophobic scores of a contact residue pair, Finally, the excluded volume effect during protein binding is taken into account in the third component of The depth of the potential ε equals 5 kT. The value of σ defines the finite distance at which the inter-particle potential is zero; it is set as 3.8 Å between two Cα atoms, 2.8 Å between a Cα atom and the representative center of a side-chain, and 2.2 Å between the representative centers of two side-chains.r (the average distance of diffusion is 10 Å), were computed in each simulation time step, Δt (1 ns). The probability of diffusion isThe association of two proteins was simulated using the KMC algorithm . The sim96DT is the translational diffusion constant of the selected protein. A periodic boundary condition was applied to any protein that reached the boundary of the simulation box. The rotational movement was then calculated after the translational movement. For rotations, within each time step, the molecule randomly rotates around each Euler angle with a value of DR is the rotational diffusion constant of the molecule and r a randomly generated number between −1 and 1.In p is calculated using the function98After the calculation of the translational and rotational movements for both proteins in the system, the energy between the two proteins was calculated using Etot are the total energy of the system before and after diffusion, respectively. The decision on whether diffusion occurred was made by comparing a generated random number with the calculated probability. At the end of each simulation step, the distances between all intermolecular interfacial pairs were calculated to determine how many native contacts were recovered. A native contact is defined as a pair of residues, i and j, in a native complex, the interaction of which contributes significantly to the total energy (Eij < −1 kT) of the complex. A native contact was considered to be recovered during simulation if the distance between the representative centers of the two residues was less than 2 Å from the distance in the native conformation. The numbers of native contacts for each complex in the 2 benchmark sets are listed in where tot simulation trajectories were completed, a success rate of ρ (ρ = Non/Ntot) was derived, in which Non is the number of times that two proteins form an encounter complex. Given the volume of the simulation box, V, the kon of protein association can be written asMultiple trajectories of KMC simulations were generated for each protein complex. The rate constant of the protein association was derived by counting how many protein complexes were associated from these simulation trajectories. The calculation of the association rate was based on the assumption that the formation of an encounter complex is rate-limiting . Each KMC simulation trajectory was terminated either when an encounter complex was formed or at the end of the simulation. After all Nttot is the maximal simulation time for each trajectory and c is a constant that converts units from molecule/nm3 to M. The detailed derivation of In kon calculated from the KMC simulations was seriously overestimated compared to the experimental value. To identify potential overestimation, a machine learning algorithm was implemented to correct the simulation results  of each of the two interacting proteins. The flexible loops are defined by the regions in the proteins whose secondary structural types are neither α-helix nor β-strand. The secondary structure type of a residue is determined by the standard DSSP algorithm by calculating the geometry of the hydrogen bonds in the backbone of a proteinrelec, which is the ratio of the electrostatic potential at the binding interface to that of the whole protein pair. The ratio is defined as follows.We observed that, in some cases, our  results . Before ij < 6 Å) in the native structure of a protein complex. By contrast, the denominator in kon of a protein complex in the training set was more than 4 times greater than the experimentally derived rate, it was assigned as “overestimated”. The same criterion of overestimation was used in a previous studyThe numerator in the above equation is the summation over all residue pairs at the binding interfaces of a native protein complex. Residue i in chain 1 and residue j in chain 2 are at binding interfaces if the representative centers of the side-chains of these two residues are close to each other  and the results for the 10-complex testing set using the trained model are described in the Results.Two specific strategies were used to test the effect of this machine learning-based correction. They were applied to minimize the possibility that the model improvement was due to the result of data fitting through parameter adjustment. The first was the cross-validation of the 49-benchmark set. In this test, the strictest method, leave-one-out, was applied to avoid the potential over-fitting of the model. During each run of the leave-one-out test, one of the 49 protein complexes was selected as the test, while the remaining 48 were classified into “overestimated’ and “non-overestimated” groups and used as the training set, and the Because the cost of performing machine learning calculations is negligible after training, we only benchmark the computational performance of coarse-graining kinetic Monte-Carlo simulations. Specifically, two systems are used. The first is the protein complex barnase/barstar (1BRS), which we used as a test system to evaluate the robustness of our KMC simulation in our study. The second is B. anthracis Protective Antigen complexed with human Anthrax toxin receptor (1T6B), which is the largest system in the 49-complex benchmark set. As a result, for the system of 1BRS, it takes 12 seconds on average to generate a trajectory of 100 ns on a regular Linux desktop. For the system of 1T6B, it takes 130 seconds on average to generate a trajectory with the same length. Based on previous studies in the literature, it is shown that a typical 100 ns trajectory of BD simulation for a protein complex of normal size takes approximately an hour on a regular Linux desktop100https://sourceforge.net/projects/pp-association-rate-prediction/. This package contains a set of Perl scripts for the batch prediction of the protein–protein association rates, a couple of executable files and their source codes, and a MATLAB prediction model to determine whether the predicted association rate for the target protein complex would be overestimated or not. It also offers a demonstration example of how to obtain the predicted association rate. These scripts work on a Linux platform, and downloading is free for academic users.The source codes of this method for the protein–protein association rate constant prediction are available for download at: How to cite this article: Xie, Z.-R. et al. Predicting Protein–protein Association Rates using Coarse-grained Simulation and Machine Learning. Sci. Rep.7, 46622; doi: 10.1038/srep46622 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


The subcellular localization of a protein is an important aspect of its function. However, the experimental annotation of locations is not even complete for well-studied model organisms. Text mining might aid database curators to add experimental annotations from the scientific literature. Existing extraction methods have difficulties to distinguish relationships between proteins and cellular locations co-mentioned in the same sentence.LocText was created as a new method to extract protein locations from abstracts and full texts. LocText learned patterns from syntax parse trees and was trained and evaluated on a newly improved LocTextCorpus. Combined with an automatic named-entity recognizer, LocText achieved high precision (P = 86%±4). After completing development, we mined the latest research publications for three organisms: human (Homo sapiens), budding yeast (Saccharomyces cerevisiae), and thale cress . Examining 60 novel, text-mined annotations, we found that 65% (human), 85% (yeast), and 80% (cress) were correct. Of all validated annotations, 40% were completely novel, i.e. did neither appear in the annotations nor the text descriptions of Swiss-Prot.LocText provides a cost-effective, semi-automated workflow to assist database curators in identifying novel protein localization annotations. The annotations suggested through text-mining would be verified by experts to guarantee high-quality standards of manually-curated databases such as Swiss-Prot.The online version of this article (doi:10.1186/s12859-018-2021-9) contains supplementary material, which is available to authorized users. Cellular Component). Many proteins function in different locations. Typically, one of those constitutes the native location, i.e. the one in which the protein functions most importantly.The subcellular location of a protein is an important aspect of its function because the spatial environment constrains the range of operations and processes. For instance, all processing of DNA happens in the nucleus or the mitochondria. In fact, subcellular localization is so important that the Gene Ontology (GO) , the staDespite extensive annotation efforts, experimental GO annotations in databases are not nearly complete . AutomatWoLF PSORT  dividing them by the largest number found in the training data (independently for each feature). If the test data had a larger number than previously found while training, its scaled float would be bigger than 1 .Is protein a marker : for example, green fluorescent protein (GFP), or red fluorescent protein (RFP). This might be a problem of the LocTextCorpus guidelines. Nonetheless, disregarding protein markers seems a reasonable step to curate databases.Is the relation found in Swiss-Prot : we leveraged the existing annotations from Swiss-Prot.N-grams between entities in linear dependency : the n-grams  of tokens in the linear sentence between the pair of entities Prot and Loc. The tokens were mapped in two ways: 1) word lemmas in lower case masking numbers as the special NUM symbol and masking tokens of mentioned entities as their class identifier ; 2) words part of speech (POS). In a 2- or 3-gram, the entity on the left was masked as SOURCE and the end entity on the right as TARGET.N-grams of syntactic dependency tree: the shortest path in the dependency parse tree connecting Prot and Loc was computed . Again, we masked the pair of entities in the path as SOURCE and TARGET. The direction of the edges in the dependency tree (going up to the sentence root or down from it) was not outputted after feature selection.The representation of the sentences as dependency graphs was inspired by Björne’s method for event extraction in BioNLP’09 . The n-gintrinsic evaluation) does not directly translate into high performance in a real task  [LocText method in both scenarios, namely, in a well-controlled corpus using standard performance measures and in the real setting of extracting novel protein localizations from the literature. Either way, and always with database curation in mind, we asked: given a scientific text (e.g. PubMed article), what protein location relationships does it attest to? For instance, a publication may reveal “Protein S” (UniProtKB: P07225) to function in the “plasma membrane” (GO:0005886). To extract this relation, it is indifferent under which names the protein and location are mentioned. For instance, P07225 can also be named “Vitamin K-dependent protein S” or “PROS1” or abbreviated “PS” and GO:0005886 can also be called “cell membrane” or "cytoplasmic membrane” or abbreviated “PM”. Further, it does not matter if the relation is expressed with different but semantically equivalent phrases . Regardless of synonymous names and different wordings, repeated attestations of the relation within the same document are all the same. In other words, we evaluated relationship extraction at the document level and for normalized entities.High performance of a method in a controlled setting (luation) . To addrtest, Loctest) was considered as correctly extracted (true positive = tp), if at least one text-mined relation  matched it, with both Prot and Loc correctly normalized: 1) Prottest and Protpred must be equal or have a percentage sequence identity 90% . For example, a tested  relation and a predicted  relation correctly match: the proteins are the same and GO:0016328  is a part of and thus more detailed than GO:0005886 (“plasma membrane”). Any other predicted relationship was wrong , and any missed known relationship was also punished . We then computed the standard performance measures for precisionrecallF-measureIn intrinsic evaluation, the annotated relations of a corpus were grouped by document and represented as a unique set of normalized entity pairs of the form , e.g. . A tested known relationship (ProtB/TrEMBL ); and 2)tp) if and only if its text offsets (character positions in a text-string) overlapped those of a known entity and its normalized identifier matched the same test entity’s normalization . Any other predicted entity was counted as fp and any missed entity as fn. In analogy, we computed P, R, and F for named-entity recognition.We evaluated relationship extraction in isolation  and as a whole (with predicted entities). Given the importance of the NER module (wrongly predicted entities lead to wrongly predicted relationships), we also evaluated the NER in isolation. We considered a predicted named entity as successfully extracted (StdErr) by randomly selecting 15% of the test data without replacement in 1000 (n) bootstrap samples. With 〈x〉 as the overall performance for the entire test set and xi for subset i, we computed: We evaluated methods in 5-fold cross-validation with three separate sets as follows. First, we split a fold into the three sets by randomizing the publications; this lessens redundancy as different publications mention different localizations. Sixty percent of documents served to train (train set), 20% to cross-train , i.e. to optimize parameters such as in feature or model selection. The remaining 20% were used for testing (test set). The performance on the test set was compiled only after all development had been completed and was thus not used for any optimization. Finally, we repeated the folds four more times, such that each article had been used for testing exactly once. We computed the standard error (LocText pipeline (i.e. NER + RE) extracted from large sets of unannotated PubMed abstracts novel protein localizations . A unique protein-location relation could be found in one or more documents. The assumption is: the more document hits, the more reliable the extracted relation. For a number of extracted unique relations, one person manually reviewed the originating and linked documents. For each “predicted” relation, we stopped our analysis when we found proof of the annotation. We deemed the prediction to be wrong if we found no textual proof in the abstracts.In extrinsic evaluation, the complete LocTextCorpus [BioNLP’09 corpus [BC4GO corpus [BioNLP’09, for instance, annotated events (relationships) not requiring the textual mention of the protein or localization entities, some location mentions contained extraneous words that were part of the phrase but not strictly part of the location names, and some locations were not only subcellular localizations but specific cells or body tissues. BC4GO contained neither exact text-level annotations of the entities nor the relationships.To train and formally benchmark the new method , we had only access to a custom-built corpus, for simplicity referred to as xtCorpus . We coul9 corpus  and the O corpus  appearedLocTextCorpus with the tagtog tool [LocTextCorpus collected 100 abstracts  with 1393 annotated proteins, 558 localizations, and 277 organisms. The organism annotation had been crucial to correctly map the protein sequence, e.g. to distinguish the human Protein S (P07225/PROS_HUMAN) from its mouse ortholog (Q08761/PROS_MOUSE). The corpus annotated 1345 relationships . When removing repeated relations through entity normalization  the new LocText method.We had previously annotated the tog tool . For thilization , the numlization . That isLocText could assist in database curation in practice, three sets of PubMed abstracts were added: NewDiscoveries_human, NewDiscoveries_yeast, NewDiscoveries_cress. For each organism, keyword searches on PubMed revealed recent publications that likely evidenced (mentioned) the localization of proteins (e.g. the search for human http://bit.ly/2nLiRCK). The search for all human-related journals published between 2012 to 2017/03 yielded ∼ 37k documents (exactly 37454). For publication years from 1990 to 2017/03, the search obtained ∼ 18k (17544) documents for yeast and ∼ 8k (7648) for cress. These documents were not fully tagged. They were only used for final extrinsic evaluation, and only after the method had been finalized. In other words, those abstracts never entered any aspect of the development/training phase.Furthermore, and to assess how the new method Textpresso system uses regular expressions and is used in database curation [Two previous methods that used machine learning techniques to model syntax also extracted protein localization relationships , 31. Howcuration . The metco-mentioned then they are related [Baseline to compare the new method against.Other methods exist that follow a simple heuristic: if two entities are  related –24. The