Informatics methods, such as text mining and natural language processing, are always involved in bioinformatics research. In this study, we discuss text mining and natural language processing methods in bioinformatics from two perspectives. First, we aim to search for knowledge on biology, retrieve references using text mining methods, and reconstruct databases. For example, protein-protein interactions and gene-disease relationship can be mined from PubMed. Then, we analyze the applications of text mining and natural language processing techniques in bioinformatics, including predicting protein structure and function, detecting noncoding RNA. Finally, numerous methods and applications, as well as their contributions to bioinformatics, are discussed for future use by text mining and natural language processing researchers. Text mining and natural language processing refer to comprehending and analyzing natural language by using computer algorithms and programs. It is an important research direction in the application field of artificial intelligence. Research on natural language processing and text mining has been reported as early as the emergence of computers. With continuous and extensive research on machine learning and data mining algorithms, existing text mining technologies have achieved good results in automatic abstraction, automatic question answering, web relational network analysis, and anaphora resolution , 2.Bioinformatics is an interdiscipline that emerged with the progress and accomplishment of the Human Genome Project. It predicts and solves live science problems related to genetics by using computer and statistical informatics. Data storage, retrieval, and analysis are the key processes in bioinformatics –7. The NText mining and natural language processing techniques are necessary to retrieve user preference knowledge from expanding databases. Therefore, researchers retrieve papers on certain topics of interest, such as determining protein-protein interactions, from PubMed using computer algorithms and programs. With the cracking of genetic codes, researchers have determined that biological sequences, particularly protein sequences, are similar to human language in terms of composition. In addition to using text mining to retrieve bioinformatics articles directly, an increasing number of researchers are regarding protein sequences as a special “text” and analyzing them based on existing text mining technologies. The relationship between bioinformatics and natural language processing is shown in The development of text mining technology plays an important role in retrieving biological literature, particularly in establishing biological information databases. A special workshop on biological literature retrieval problems was conducted during the Annual Meeting of the Association for Computational Linguistics and the Annual International Conference on Intelligent Systems for Molecular Biology in 2005 to discuss literature mining problems related to bioinformatics. Extracting protein-protein interactions and the relationship between gene functions and diseases are two leading application subjects.Extracting the protein interaction network is an important research topic in bioinformatics and systems biology –14. In pInitially, researchers extracted protein-protein interactions through statistical and counting methods. They manually created dictionaries of protein names and then searched abstracts that involve elements occurring at least twice. On this basis, researchers determined that associated proteins interact with one another . Some reExtracting protein-protein interactions has been a research hot spot in bioinformatics for a long time and has attracted an increasing number of researchers in the fields of text mining and natural language processing. First, the grammar of literature abstracts is analyzed more carefully, rather than making a simple statistics of dictionary words. Kim et al. converted a complicated semantic structure analysis into calculating the shortest path in a graph by creating a nucleus . SimilarExtracting protein-protein interactions involves searching for two proteins in the text and determining whether they interact with each other. Similarly, extracting the relationship between gene functions and diseases also involves searching for gene names and disease names simultaneously in the literature and then determining whether a particular gene is related to a certain disease .In general, such extraction process can be divided into three steps. First, the abstracts of associated papers are searched through comparison with a dictionary. Second, the search scope has to be expanded forward and backward sometimes based on the location of the related word or clause to ensure accuracy. Finally, facts are evaluated using grammar analysis methods or machine learning methods. Such extraction methods frequently yield good results for special genes and diseases. Bui et al. examined the relationship between drugs and HIV variation in PubMed . Jiang eA considerable amount of bioscience literature has been published. Searching for interacting proteins and examining the relationship between genes and diseases are only two application cases. Text mining technology is required to obtain answers to many other bioscience and bioinformatics problems in various databases, such as PubMed.Biological literature mining and related problem solving have to cope with two major problems, namely, recognizing name entities and extracting relations. These problems are mainly solved by (1) methods based on linguistic analysis , (2) metSeveral important databases are also selected with text mining. STRING  and BioGMoreover, converting PubMed database into an Extensible Markup Language relational database  and a fuDNA and protein sequences are a meaningful genetic language and are regarded as the sealed book of life. Therefore, an increasing number of natural language processing and text mining algorithms are being applied to study bioinformatics. For example, latent semantic analysis was applied to protein remote homology detection , 46, andα-helix, β-lamella, and protein disordered regions. Predicting the α-helix and β-lamella regions is the same as predicting the secondary protein structure.Protein structure determines function . Hence, If a protein sequence is regarded as a natural language, then analyzing the type of protein in a region is similar to calibrating grammar in natural language processing. First, the secondary protein structure is predicted by combining rules and statistics –52. HowePredicting the protein disordered region is also conducted. This region refers to the area without a stable or unique 3D structure in the protein space structure. Many text mining and machine learning methods, including ANN –62, SVM Predicting protein function is one of the most basic research topics in bioinformatics. It involves predicting protein-protein interactions and interaction sites , 69, locThe protein sequence is easy to determine. Similar to natural language, the protein sequence has many complicated rules. However, summarizing and understanding the rules of protein sequences are difficult. Therefore, analyzing and predicting the “protein language” expressed by amino acid sequences by using computational linguistics and machine learning methods are necessary. Through these procedures, we may be able to understand the functions of protein sequences.Predicting protein-protein interactions is one of the most basic research topics in protein functions. Many researchers are committed to predicting whether two protein sequences exhibit interactions. To date, many machine learning methods have been applied, including SVM , kernel Alignment is also an important topic in natural language processing. DNA or RNA sequences can also be viewed as text. Sequence-based multiple sequence alignment methods can be used only at the sequence similarity level. The secondary structures of ncRNAs are usually more conserved than their sequences , 117; foLocARNA  can prodAlthough comparative methods perform well in most cases, they have three intrinsic limitations: (1) they are highly dependent on the availability of homologous sequences or structures and cannot make predictions when no relevant sequence similarity or structure similarity is available; (2) they cannot correctly identify real ncRNAs that have low homology with known ncRNAs; and (3) they can identify only ncRNAs that are homologous with members of known ncRNA classes but cannot identify members of novel ncRNA classes. Most lncRNAs (long noncoding RNAs) cannot be predicted using comparative methods because they do not have specific structures or sequence similarity. These limitations mean that comparative methods display low specificity for identifying ncRNAs. The multiple sequence alignment tools that are currently available are listed in The noncomparative methods are independent of homologous information and can, therefore, detect nonconserved ncRNAs. Most noncomparative methods employ machine learning techniques to make the predictions , which aBecause of the importance of RNA structure, several computational RNA folding tools have been developed, such as mfold, RNAfold, vsfold, evofold, and sfold. Generally, these algorithms determine the folded secondary structure from and input sequence by optimizing the intermolecular base pairing to minimize the free energy. Some miRNA identification methods are shown in As research on natural language and text mining methods develops, different application fields will be the key to future studies. Interdisciplines represented by bioinformatics are becoming the focus of an increasing number of information science researchers. The application of text mining technologies and methods in bioinformatics study will become the focus of text mining researchers. Meanwhile, bioinformatics researchers have to learn text mining technologies intensively to solve specific bioinformatics problems.In retrieving biological literature, apart from the aforementioned prediction of protein-protein interactions and gene-disease relationship, many problems, particularly those that require updating literature retrieval results, such as the relationships between adverse drug reaction and molecule composition as well as among single nucleotide polymorphism sites, diseases, and adverse drug effects, require the use of text mining to search for related knowledge in a literature database.In bioinformatics, nearly all studies related to proteomics and predicting protein structure according to amino acid sequences can be conducted using text mining and natural language processing technology. Many mature texts mining technologies, such as word frequency statistics, condition random fields, HMM, and context-free grammar, have been successfully applied to predict secondary protein structures, irregular regions, interactions, and interaction sites. However, the latest research results in text mining and natural language processing should be verified by applying them in protein and DNA languages. No effective computation method is available yet for predicting third and fourth protein structures, protein homology remote detection, protein disordered region detection, interaction network establishment, and drug target prediction. Information science researchers should develop and provide more effective algorithms. In addition, new machine learning and text mining methods  have been proposed and will be applied in biological literature retrieval and bioinformatics. At present, recommending systems based on feedback has become a new hot spot problem in retrieving biological literature. And the Hadoop technique for big data is another hot spot for biology sequences .The development of bioinformatics relies on information science. In particular, text mining and natural language processing researchers should provide a more extensive application space. Researchers of text mining algorithms should develop more effective intelligent algorithms based on the characteristics of biological data. This study does not only summarize text mining methods used in bioinformatics and corresponding problems, but it also provides related websites of successful prediction software. Recently, text mining researchers who are involved in bioinformatics can test and compare different types of software. The authors hope that the number of text mining researchers who can apply their own methods in bioinformatics will increase, which will facilitate the development of bioinformatics and even genetic studies.


Plasmodium falciparum. Human-parasite surface protein interactions play a critical role in this process. Although several interactions between human and parasite proteins have been discovered, the mechanism related to invasion remains poorly understood because numerous human-parasite protein interactions have not yet been identified. High-throughput screening experiments are not feasible for malarial parasites due to difficulty in expressing the parasite proteins. Here, we performed computational prediction of the PPIs involved in malaria parasite invasion to elucidate the mechanism by which invasion occurs.The invasion of red blood cells (RBCs) by malarial parasites is an essential step in the life cycle of D. melanogaster alone when information related to the six species was used. Prediction performance was assessed using protein interaction data from S. cerevisiae, indicating that the predicted results were reliable. We then used the estimates of DDI probabilities to infer interactions between 490 parasite and 3,787 human membrane proteins. A small-scale dataset was used to illustrate the usability of our method in predicting interactions between human and parasite proteins. The positive predictive value (PPV) was lower than that observed in S. cerevisiae. We integrated gene expression data to improve prediction accuracy and to reduce false positives. We identified 80 membrane proteins highly expressed in the schizont stage by fast Fourier transform method. Approximately 221 erythrocyte membrane proteins were identified using published mass spectral datasets. A network consisting of 205 interactions was predicted. Results of network analysis suggest that SNARE proteins of parasites and APP of humans may function in the invasion of RBCs by parasites.In this study, an expectation maximization algorithm was used to estimate the probabilities of domain-domain interactions (DDIs). Estimates of DDI probabilities were then used to infer PPI probabilities. We found that our prediction performance was better than that based on the information of We predicted a small-scale PPI network that may be involved in parasite invasion of RBCs by integrating DDI information and expression profiles. Experimental studies should be conducted to validate the predicted interactions. The predicted PPIs help elucidate the mechanism of parasite invasion and provide directions for future experimental investigations.The online version of this article (doi:10.1186/s12859-014-0393-z) contains supplementary material, which is available to authorized users. The ability of a malaria parasite to invade red blood cells (RBCs) is a key step for its survival and pathogenesis. The malarial parasites can successfully invade RBCs by several mechanisms, including rhoptry and microneme protein secretion, tight junction formation, and acto-myosin motor assembly ,2. PreviP. falciparum [Although high-throughput biological experiments such as yeast two-hybridization and avidity-based extracellular interaction screen can be conducted to reveal interactions between parasite proteins and human proteins ,7, therelciparum . For thilciparum . Computalciparum . Positivlciparum . CandidaP. falciparum were predicted to elucidate the protein interactions involved in parasite invasion of RBCs. Considering that a protein domain serves as a unit of protein-protein interactions and is evolutionally conserved, a model was developed to relate protein interaction probabilities with domain interaction probabilities. In the present study, an expectation maximization (EM) algorithm proposed by Liu et al. was used to estimate the probabilities of domain-domain interactions (DDIs) [fp) and false negative rate (fn) were specified. DDI estimates were then used to infer plausible interactions between human and parasite membrane proteins.In this study, membrane protein interactions between human and s (DDIs) . The EM s (DDIs) . In this-domain relationships of each protein in six species, including Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Homo sapiens, Mus musculus, and Schizosaccharomyces pombe, were extracted from the PFAM database [-domain relations, we defined a significant protein-domain relationship (PDR) as one with E-value less than 1E-5 for proteins in A. thaliana, C. elegans, D. melanogaster, H. sapiens, M. musculus, and less than 1E-4 for proteins in S. pombe. Proteins with significant PDRs were represented by their respective Entrez gene ID or gene symbol. The physical protein-protein interaction data of these six organisms were collected from the BioGRID database [A. thaliana, 3,052 interactions from C. elegans, 21,752 interactions from D. melanogaster, 94,396 interactions from H. sapiens, 7,409 interactions from M. musculus, and 3,828 interactions from S. pombe. Detailed information on the collected protein interactions for each species used in our study can be found in Additional file The proteindatabase . To redudatabase . We onlyD. melanogaster, we constructed a DDI matrix containing information of interactions among 1,515 domains. The second high confidence dataset consisted of protein interactions from S. cerevisiae, in which physical protein interactions have been widely investigated. The PPIs in S. cerevisiae were also collected from BioGrid database. We defined PDR as significant when its E-value was less than 1E-4. After removing proteins containing domains not found in the six species studied here, we obtained 49,084 interactions among 3,960 proteins. We considered these 49,084 interactions as positive interactions and the remaining protein pairs were considered to be non-interacting (negative interactions). The last high confidence dataset was a small-scale dataset that contained 456 experimentally determined interactions between human and parasite proteins [Three high confidence datasets were constructed and used to evaluate the reliability of prediction. The first HC dataset contained DDIs collected from iPFAM and 3DID databases ,17. A toproteins . After rPinter) between proteins Pi and Pj was expressed as follows:Pijk represents the protein pair Pi and Pj in organism k (k = 1… 6) and λmn is the interaction probability of domain pair Dm and Dn. We defined Dm and Dn interacted in the protein pair Pi and Pj and Pi and Pj in organism k. The probability of interaction between proteins Pi and Pj in the experiments was expressed as follows:fp and fn represent the FPR and the FNR of protein interaction data, respectively. fp was calculated using the formula below when fn and the average number of interaction partners were designated:m and t represent the protein number and the average number of interacting partners, respectively, and No is the number of observed PPIs. We assumed that fp and fn are similar across the six species. The likelihood function characterizing the probability of the observed protein interaction data across six species was expressed as follows:Pi was interacting with protein Pj in species k, Oijk = 1; otherwise, Oijk = 0. After specifying fn and fp, we can estimate λmn using the EM algorithm. The EM algorithm consisted of E- and M-steps. In the E-step, We estimated DDIs using the EM algorithm ,19. The After obtaining the N is the number of protein pairs containing a domain pair . The initial value of λmn was expressed as λmn by iterating between E- and M-steps to obtain the maximum likelihood estimation of λmn. If λmn was updated in the EM algorithm; however, computational consumption was very high.where λmn and high computational consumption in the E-step were obtained. To simplify computation, we assigned all of the initial low λmn to zero. In the case of large protein pair samples (n >30) containing a domain pair , if λmn fulfilled the following condition:Although several domain pairs were found in the hundreds of protein pairs, only a few protein pairs were found to be interacting. As a result, an initial low λmn was set to zero and not used in the E-step. In the present study, p = 0.00143 was the observed protein interaction probability and computed using the number of the experimentally confirmed PPIs to divide the total number of protein pairs across the six species. Therefore, we only considered λmn with initial values that were significantly higher than the observed protein interaction probability (p).the value of λmn were then obtained and used for protein interaction prediction.The EM algorithm was implemented in Matlab 2012b for two months by using the parallel computing toolbox. EM steps were repeated until the difference between two consecutive steps was <0.01 or the number of repeats >40. Estimates of N is the length of signal, and k is the frequency. Using this method, we can determine the frequency of a particular gene and its amplitude. The magnitude (M) at frequency ω = 1 of each gene was determined. The genes significantly correlated with cell cycle with magnitude M >0.5 at frequency ω = 1 were selected. To determine the peak expression time of each gene, we calculated phase value (P) by using the major frequency, and the peak expression time was computed using the following equation:FFT has been used to detect genes relevant to specific biological processes, such as cell cycle and cardian clock -22. FFT With Fourier analysis, periodic genes within a genome can be identified, and the peak expression time of each gene can be determined.P. falciparum [k nearest neighbor (KNN) method [We performed FFT analysis on time course microarrays in the asexual intraerythrocytic developmental cycle (IDC) of lciparum . These e) method . Before Proteins located in the membrane of human erythrocyte or malaria parasite should contain a transmembrane domain (TM) in their structures. They may or may not contain cleavable signal peptides in their sequences. To identify the human and malaria parasite membrane proteins that may interact, we used SignaIP and TMHMM to predict membrane proteins ,26. The The ROC curve representing the relationship between true positive rate (TPR) and FPR was used to evaluate the reliability of predicted protein interactions . A protePositive predictive values (PPV), also known as precision, were introduced to indicate the accuracy of prediction. PPV was computed as follows:If the predicted result showed high PPV, a high success rate could be obtained in experimental validation of predicted protein interactions.http://david.abcc.ncifcrf.gov/) [p-value was <0.05.To understand the biological meaning of a large list of genes identified using the FFT method, we performed GO term enrichment analysis on these genes via the DAVID web server (rf.gov/) . The GO Protein domains are the structural units of proteins, and PPIs are mostly achieved through DDIs. Therefore, we first needed to estimate the DDI probabilities before inferring PPI probabilities. In our analysis, an EM algorithm was introduced to estimate the probability of each DDI.D. melanogaster. Considering that the protein interaction data contain several errors, we cannot determine these errors because actual protein interactions are unknown. In our analysis, a range of error rates were examined to assess the accuracy of this maximum likelihood method. Table fn and fp used in our analysis. As shown in Figure fn and fp were assigned to three different values, which suggested that prediction accuracy was robust with respect to fn and fp. This finding is consistent with the results observed by Liu et al. [This algorithm was initially applied to predict the DDI probabilities from the iPFAM and 3DID databases based on information pertaining to u et al. .Table 1λmn as zero (λmn significantly higher than the observed PPI probability (p = 0.00143) was used in EM algorithm. This simplification indicated that only parts of non-zero λmn were used for computation. To assess the effect of this alteration on the prediction accuracy of the EM algorithm, we fixed fn and fp, and the ROC curves obtained using totally non-zero λmn were then compared with those obtained using partly non-zero λmn. The ROC curves were nearly identical when the cut off value was set at ≥0.05 . These results suggested that the PPIs inferred using the estimates of DDI probabilities were reliable. At a threshold of 0.01, 299,602 proteins were predicted to be interacting . The low TPR may be caused by several protein interactions in this network being false positives, since the observed interaction probability (p = 0.0187) was apparently larger than the interaction probability observed in the aforementioned six species (p = 0.00143). Low PPV suggests that integration of other information is necessary to improve our prediction accuracy.Considering that significantly limited interactions were observed between human and parasite membrane proteins, we could not easily evaluate the prediction accuracy of our method. To demonstrate the viability of our method in predicting interactions between proteins from different species, 456 experimentally confirmed interactions between human and parasite proteins were used. After removing proteins not fulfilling the PDR conditions, we obtained a network consisting of 132 interactions between 66 parasite membrane proteins and 107 human membrane proteins. The observed interaction probability of this small-scale network was We excluded falsely predicted PPIs by integrating the expression profiles of merozoite and human erythrocytes to improve the PPV of PPI prediction and to provide the direction for future experimental studies. The interactions between merozoite and human erythrocyte membrane proteins are mostly responsible for parasite invasion. Therefore, highly expressed proteins in merozoites and human erythrocytes should be identified to improve prediction accuracy.P. falciparum was highly dynamic, we performed fast Fourier transform (FFT) analysis to extract the periodic genes that were highly expressed in the schizont stage. FFT is an approach used to compute a discrete Fourier transform of a finite length signal; this approach has also been applied for periodic genes whose expressions oscillate at one or more frequency. FFT converts an expression signal in a time domain into a frequency domain. Significant frequencies could be obtained by using this method. For example, the apical merozoite antigen (PfAMA1) plays a critical role in parasite invasion [Considering that the IDC gene expression of invasion . This geω = 1 were considered to be significant periodic probes. The peak expression time of each periodic gene was calculated using a previously described method (Tpeak calculation, To identify highly expressed genes in merozoites, we only need to extract the genes highly expressed in the schizont stage because mature schizonts consist of tens of merozoites. We performed FFT analysis on a published microarray dataset . Probes Among these 800 genes, approximately 330 genes were annotated with molecular functions. The results of enrichment analysis indicated that the genes implicated in protein serine/threonine kinase activity were significantly enriched Table . This reTo infer interactions between merozoite and human erythrocyte proteins, we focused on membrane proteins of these 800 proteins expressed highly in the schizont stage. We overlapped these 800 proteins and 490 parasite membrane proteins previously identified and found that 80 membrane proteins were highly expressed in the schizont stage Figure c. To ideOn the basis of previously computed 62,197 probabilities between human and parasite membrane proteins, we obtained a network consisting of 80 parasite proteins and 221 human proteins after the proteins not expressed in the schizont stage and those not found in RBCs were filtered out. A total of 960 interactions with probabilities >0 were found. At a threshold of 0.01, 467 interactions were predicted. Considering that experimentally discovered surface protein interactions are limited, we could not evaluate the accuracy of our prediction. Six non-interacting protein pairs were manually collected from the third high confidence dataset. These non-interacting protein pairs consisted of two parasite membrane proteins and three human membrane proteins. Our prediction results indicated that all of the probabilities of these protein pairs were 0, suggesting that no interactions were found between those proteins. Thus, our predicted results are consistent with the experimental results. Further experimental validation is needed to fully assess the accuracy of our predictions.To increase the PPV of prediction and provide directions for future experimental studies, we set the threshold at 0.05 and built a small network containing 205 interactions Figure a. Among Eight parasite proteins in this network exhibited degrees of interaction more than six and were selected for further analysis Table b. Some oOn the basis of these network analysis results, we chose parasite SNARE proteins and human APPs for further biological experimental studies.In the present study, we sought to elucidate the molecular basis of invasion of RBCs by malarial parasites. We used the EM algorithm to estimate the DDI probabilities based on currently available large-scale protein interaction datasets, and then used the estimates of DDI probabilities to predict interactions between human RBCs and merozoite membrane proteins. We also integrated gene expression information to reduce false positives and thus improve our prediction accuracy. Using FFT, we identified the genes highly expressed in the schizont stage. By combining estimated PPI probabilities and gene expression profiles, we predicted a network consisting of 205 PPIs between parasite and human membrane proteins. The results of further analysis suggested that SNARE proteins in parasites and APP in humans may play an important role in the invasion of erythrocytes.Although we inferred possible interactions between human and malaria parasite proteins, our prediction did not capture several discovered protein interactions related to parasite invasion of RBCs because we only considered possible interactions between proteins that satisfy the PDR condition. For instance, previous experimental investigations reported that several interactions between parasite proteins and human proteins, including MSP1-Band3 and EBA175-Glycophorin A ,39, are In addition, interactions mediated by secretory or peri-membrane proteins are also not predicted because they were excluded from our analysis for the reason that most secretory parasite proteins contain domains not found in all six organisms and identification of human peri-membrane proteins is not feasible. In our study, we only predicted interactions between membrane proteins in parasites and human RBCs. However, several secretory parasite proteins and human perimembrane proteins are known to be utilized by parasites in the invasion of RBCs. For instance, PfRh5 proteins secreted by parasite rhoptry are involved in parasite invasion of erythrocytes by directly interacting with the surface protein Basigin in RBCs . The humD. melanogaster alone, which allowed us to make more informative inferences regarding protein interactions. A network composed of interactions between human and parasite proteins was then inferred. Assessment of our predictions using a small-scale protein interaction dataset indicated that our prediction accuracy was lower than that obtained in S. cerevisiae. This phenomenon may be caused by several protein interactions being false positives in the small-scale dataset, and suggested that other information, such as gene expression profile and gene ontology information, should be integrated to improve prediction accuracy [We found that the results of incorporating information from six species outperformed those obtained from accuracy . In our Despite these limitations, our predicted interactions revealed that SNARE proteins that participate in protein secretion may be involved in malaria parasite invasion of erythrocytes. This result is consistent with previous reports that protein secretion via microneme and rhoptry are necessary to induce parasite invasion of RBCs . PredictIn the present study, the EM algorithm was used to estimate probabilities of DDIs, which were then applied to infer possible protein interactions between human RBCs and parasite merozoite membrane proteins. Gene expression data was integrated to filter out false positives. FFT was introduced to identify genes expressed highly in the schizont stage, while published data was utilized to search for membrane proteins of RBCs. A network consisting of 205 PPIs, including several novel interactions, was obtained. Our results help elucidate the molecular mechanism of parasite invasion and provide promising research directions for further experimental studies.


Most biological processes are carried out by protein complexes. A substantial number of false positives of the protein-protein interaction (PPI) data can compromise the utility of the datasets for complexes reconstruction. In order to reduce the impact of such discrepancies, a number of data integration and affinity scoring schemes have been devised. The methods encode the reliabilities (confidence) of physical interactions between pairs of proteins. The challenge now is to identify novel and meaningful protein complexes from the weighted PPI network. To address this problem, a novel protein complex mining algorithm ClusterBFS (Cluster with Breadth-First Search) is proposed. Based on the weighted density, ClusterBFS detects protein complexes of the weighted network by the breadth first search algorithm, which originates from a given seed protein used as starting-point. The experimental results show that ClusterBFS performs significantly better than the other computational approaches in terms of the identification of protein complexes. Protein complexes are molecular aggregations of proteins assembled by multiple protein-protein interactions. Many proteins are functional only after they are assembled into a protein complex and interact with other proteins in this complex –4. The vHigh-throughput experimental technologies, along with computational predictions, have produced a large amount of protein interactions –11, whicIn order to address that particular question, a number of data integration and affinity scoring schemes have been devised , 23–30. In this study, we propose a novel algorithm to derive yeast complexes from weighted (affinity-scored) PPI network and call it ClusterBFS (Cluster with Breadth First Search). ClusterBFS builds clusters in terms of breadth first search algorithm, starting from local seeds and adding nodes that maintain the weighted density of the clusters. The experimental results show that our ClusterBFS method outperforms existing computational methods, such as MCL , ClusterG =  with a confidence score 0 < wu,v ≤ 1, for every edge  ∈ E. For any two vertices, u and v without an edge between them, we set wu,v = 0. For each set of vertices S ⊂ E, we define its weighted density as the sum of the weights of the edges among them divided by the total number of possible edges :Given a weighted network, the goal of our algorithm is to output a set of disjoint dense subgraphs. We model the network as a undirected graph  Td; otherwise, the cluster is output. The growth process is repeated from different seeds to form multiple, possibly, overlapping groups. Although some overlaps are likely to have biological importance, groups overlapping to a very high extent in comparison to their sizes should likely be discarded. We quantify the extent of overlap between each pair of groups and discard the smaller group, if the overlap score , ClusterBFS gets the highest F-measure score. In this interval, the F-measure score remains unchanged. Therefore, the parameter Td is set as 0.1 in our experiment.In this experiment, we study the effect of the thresholdsrger 0.8 . So we athms See . BesidesProtein complexes are important for understanding principles of cellular organization and function. Therefore, much work has been concerned with the prediction of protein complexes from the PPI networks. However, the PPI datasets from high-throughput techniques are flooded with false interactions. In response, some research groups propose a number of data integration and affinity scoring schemes and construct various weighted networks.F-measure, MMR, colocalization, and coannotation measures. The evaluation of our predictions demonstrates the following advantages of ClusterBFS over the compared approaches. First, ClusterBFS has achieved significantly higher F-measure and MMR than the existing methods. Thus, our predicted complexes match very well with benchmark complexes. Second, ClusterBFS also performs very well in terms of other measures such as coannotation and colocalization, indicating that ClusterBFS can predict protein complexes very accurately. Last but not least, as mentioned above, the real complex set CYC2008 contains a lot of small complexes and so it is necessary to mine them. In comparison with the other approaches, ClusterBFS discovers much more small size complexes. Our identified complexes, therefore, could be probably the true complexes to help the biologists to get novel biological insights.In this research, we devise a novel algorithm called ClusterBFS to identify protein complexes from the weighted PPI networks. ClusterBFS derives from the breadth first search method and constitutes protein complexes that originate from a protein seed based on the weighted density. In order to characterize these clusters as protein complexes, we check their biological relevance. This is achieved through some criteria such as


Most physiological processes in living systems are fundamentally regulated by protein–ligand interactions. Understanding the process of ligand recognition by proteins is a vital activity in molecular biology and biochemistry. It is well known that the residues present at the binding site of the protein form pockets that provide a conducive environment for recognition of specific ligands. In many cases, the boundaries of these sites are not well defined. Here, we provide a web-server to systematically evaluate important residues in the binding site of the protein that contribute towards the ligand recognition through in silico alanine-scanning mutagenesis experiments. Each of the residues present at the binding site is computationally mutated to alanine. The ligand interaction energy is computed for each mutant and the corresponding ΔΔG values are calculated by comparing it to the wild type protein, thus evaluating individual residue contributions towards ligand interaction. The server will thus provide a ranked list of residues to the user in order to obtain loss-of-function mutations. This web-tool can be freely accessed through the following address: http://proline.biochem.iisc.ernet.in/abscan/. These binding sites vary in size ranging from six to thirty residues depending upon the size and the nature of the ligand. In most cases, the contribution of the individual amino acids towards the binding of a given ligand is not well understood. A well-established method of demonstrating the importance of a residue at the site is to create point mutants through site-directed mutagenesis2. Efforts towards characterization of entire functional site include tools such as alanine scanning mutagenesis (ASM)3 where each residue is mutated to an alanine and its effect on the function is evaluated. ASM is indeed a well-used technique in experimental biology and has been successfully applied to the problems of protein folding and stability4, protein-protein6, and protein-ligand7 interactions. The experimental success of this technique has resulted in further developments, including high-throughput and low-cost variants8, greatly expanding its reach. Yet, given the time, cost and effort required for carrying out experimental biochemistry, a large majority of proteins are yet to be studied through this method.Currently 9. Spurred by the successes and widespread adoption of the ASM technique, various computational resources now exist forin-silico alanine scanning. Prominent examples include Modeller10 and the Rosetta software suite11. However, most packages are command-line oriented and are out of reach for researchers. Alanine scanning webservers with intuitive user interfaces such as Robetta webserver12, the Rosetta Design web-server13, ROSIE14, FOLDX15, BeATMuSiC16, DrugScorePPI17 exist for the problems of protein folding, protein stability and protein-protein interactions. Although, there are workflows to evaluate ligand-binding energetics which require significant computational time and setup through free-energy calculations involving Molecular Mechanics/Generalized Born Surface Area method (MM-GBSA)20, there is however, no intuitive web-tool available for analyzing alanine-scanning mutations of small-molecule binding site residues in real time. A common requirement for an experimental biochemist is to identify which amino acids to mutate in the protein to generate loss-of-function mutants. A web-tool to cater to that specific need will therefore be highly useful. The analysis will also provide deep insights into critical residues for interaction, residue pairs or sets that when mutated will abolish ligand binding and provide analytical insights for lead refinement in the process of drug discovery, as well as understand drug resistance due to mutations.Due to availability of a variety of structural bioinformatics tools, it is now feasible to carry out alanine scanning mutagenesis computationally10 for site-specific alanine mutagenesis and Autodock21 for energetic evaluation of protein-ligand complexes.We present a computational workflow and webserver, Alanine Binding Site-Scan (ABS-Scan), for automated alanine-scanning mutagenesis of protein-ligand interface residues. The workflow combines the libraries of widely used software packages including Modellerin-silico alanine scanning mutagenesis is carried out. Once the input parameters are obtained, the Modeller library is used to perform site-specific mutagenesis on all selected residues, coupled with steps of energy minimization22. This consists of initial steps of conjugate gradient (200 iterations with minimum atom shift of 0.001Å), followed by 200 steps of molecular dynamics simulation with steepest descent carried out at different temperatures. The initial restraints for the mutated model are derived from the wild-type protein structure. The analysis and results derived from alanine scanning mutagenesis relies on two assumptions: (a) The introduced point mutation does not drastically change the structure of the protein and (b) the mode of ligand interaction in point mutant is the same in comparison to wild-type complex. Care is taken to ensure that there are no steric clashes between the protein/ligand atoms during the process of minimization. The quality of the protein structures generated is estimated through Discrete Optimized Protein Energy (DOPE) score23, a statistical potential score that is calculated for each of the mutant. This scoring scheme is based on the improved reference consisting of non-interacting atom pairs in a homogenous sphere with radius dependent on sample native structure. The score therefore reflects the feasibility of interactions and the compactness of the modeled structure.This workflow allows a user to submit a protein-ligand complex of their interest . The use21, to calculate the energetics of a protein-ligand complex. The force-field is used here only to score the pose of protein-ligand interaction and no docking is performed. By default, ‘check_hydrogens’ flag is kept ‘on’ while preparing the receptor and Gasteiger charges are used for proteins and ligand. The contribution from a protein residue is determined by difference in interaction score of mutant and wild-type protein . These results are graphically presented to the user, along with a ranked list of residues in the given site that could be experimentally explored for site-directed mutagenesis. A Jmol applet displays protein-ligand interactions with residues colored according to the computed extents of contribution towards interaction, while a table simultaneously displays inter-molecular energy scores. We also provide a help-section explaining the results along with selected examples.Each mutated structure, will then be scored by using Autodock 4.1 force fieldwww.csardock.org/). Decoys in this dataset contain artificial docked complexes of protein with ligands having similar chemical properties to native ligands, but known not to interact with the protein. The protocol could be successfully applied on 288 of 343 protein-ligand native and decoy complexes. The distribution of average ∆∆G scores obtained through ABS-Scan analysis for residues in the binding site for decoy dataset is seen to be different from the native protein-ligand complexes (24 and comprises 195 protein-ligand complexes (PDBbind core dataset). Around 135 of these protein-ligand complexes could be successfully processed using ABS-Scan workflow. In this case, an average ∆∆G score of 0.387 was observed for each mutated residue at the binding site. Hence, to determine the sensitivity of ABS-Scan, a cut-off of 0.5, which is a more stringent value, is chosen. ABS-Scan is seen to effectively discriminate between the decoy and the native complexes of CSAR dataset  in ~67% of the cases (∆∆G ≥ 0.5). This clearly indicates that residues important for ligand interaction can be identified through this protocol , specific substrate/product assaysetc. These measures cannot be normalized to derive values having uniform units for direct comparison. We describe three such examples here, each with different experimentally reported mutant evaluation scores and the predicted ∆∆G values for the same as case studies to highlight the heterogeneity associated with the data.Each of the above experiments involving alanine-scanning mutagenesis reports different mutant evaluation scores. The measures reported to test the fitness of the mutants include various attributes such as Ket al.26 reports that binding site residue in direct contact with the ligand influences the rate determining step of the enzymatic reaction. In this case, the alanine scanning experiments performed on the residues in the binding site that recognize progesterone and testosterone reports the Kd values. The ABS-Scan analysis performed on 3-alpha hydroxysteroid dehydrogenase in complex with both testosterone and progesterone also predicted the residues W227 , Y310 , L54 to be important for ligand recognition. A good correlation was observed (0.829 for testosterone and 0.704 for progesterone) between the reported Kd value of the mutants and the corresponding predicted ∆∆G score.A study on testosterone binding site of rat 3-alphahydroxysteroid dehydrogenase (PDBID: 1AFS) by Herediaet al.27. Since there was no structural information available for the analogs complexed to vitamin-D receptor, four of the vitamin-D analogs were docked on the receptor at the vitamin-D native binding site using Rosetta 3.4 docking protocol28. All the poses obtained were analyzed using ABS-Scan to determine the residues crucial for interaction of particular ligands. Since this is a nuclear receptor protein, a transcriptional activity assay was used in original study to evaluate the effect of mutants generated. The effect of each vitamin-D receptor mutant was measured by the downstream transactivation assay that quantifies luciferase activity under the influence of VDR (Vitamin-D Receptor promoter) promoter sequence. In this case, if the mutation affects the binding of ligand, correspondingly the expression of luciferase would reduce by a factor that can be quantified. A good negative correlation was also observed with all the four analogs complexed to vitamin D-receptor and atleast four residues - L233, W286, R274 and H397, important for interaction with all the analogs had ∆∆G score > 0.5. L233 and W286, present in H3 (helix 3) and β sheet are reported to have hydrophobic interactions with B and C rings of the ligand whereas R274 present in H4 (helix 4) is observed to have hydrogen bond interaction with 1α-OH group of the ligand.A two-dimensional alanine scanning mutations were performed to understand the structure-function relationship between vitamin-D receptor (PDBID: 1IE9) and vitamin-D analogs by Shimizu7G caps (7-methyl guanosine caps) to 2,2,7-trimethylguanosine (TMG) caps. In the original study29 around 37 point mutations were introduced into human Tgs1 (PDBID: 3GDH) to study the interaction profile with mGTP (7-methyl guanosine tri-phosphate) and AdoMet (S-adenosyl methionine). The fitness of mutants generated in this case was evaluated by using the methyltransferase assay that determines the percentage of methylation by quantifying the levels of m7GDP to m2,7GDP. The residues - R807 and K646, reported to be the most affected mutants, are also predicted by ABS-Scan to be essential, with the highest predicted ∆∆G score of 3.63 and 3.39 respectively. These positively charged residues (R807 and K646) are observed to interact with α and β phosphate groups of m7GTP. The π -cation stacking observed between W766 and the m7G was also predicted to be crucial (∆∆G score of 2.66) and correspondingly no methylated products were detected for this mutant through methyl-transferase assay.A similar study was carried out on human trimethyl-guanosine synthase enzyme (Tgs1) that converts mhttp://proline.biochem.iisc.ernet.in/abscan/examples.The details of the case-studies described above along with the results of the analysis can be accessed on the example section of the web-tool -https://github.com/praveeniisc/ABS-Scan). The script has been tested on Intel 2.83 GHz quad-core system running 32 bit linux OS(Ubuntu 12.04) with Modeller10, MGL AutodockTools30 & Pymol (http://pymol.org) installed. For the web-server d3.js library has been used for displaying the plots. Jmol Applet has been used to visualize the protein-ligand interaction.The web-server was implemented using hypertext preprocessor (PHP). Autodock, Modeller and Pymol libraries have been used for modeling the mutation and evaluating the energetics. Integration of these back-end libraries for presentation as a functional and intuitive user interface is accomplished using Shell, Python, Java, HTML and PHP scripts. The web-server is platform independent and will run on any machine having internet access with browser installed. For the advanced users, a command-line interface in the form of a single python script can be accessed from github repository  which is important for evaluating energetics. Enumerating all important structural water molecules involved in the ligand interaction is also highly dependent on the resolution of the crystal structure. Hence, an advanced option is provided to the user for uploading the PDBQT format of the ligand, to account for cases where the ligand contains unusual atom types, metal ions or uses bridge-water molecules for interaction. For practical purposes, the bridge water molecules can be considered to be the part of ligand and these can be incorporated into the pdbqt file of the ligand. As an example, ABS-Scan analysis was carried out on protein lysine methyltransferase (PDB: 3S7B) complexed with S-adenosyl methionine33 through four bridge water molecules. These four bridge-water molecules can be incorporated into the ligand pdbqt file and uploaded with the help of an advanced option provided on the server. The protocol correctly identified GLU135 and ASN182 as significant contributors to ligand binding through formation of water bridges. The output can be accessed through the example section of the web-server.The input required for the server is the structure of a protein-ligand complex in PDB format. Users can either provide the four-letter PDBID or upload the PDB structure file of the complex. An option is provided to define the cut-off distance and select the ligand to obtain binding site residues which would be mutated to alanine for evaluating the interaction energetics. A default distance cut-off of 4.5 Å is set to select all the residues whose atoms lie within this distance from any ligand atom. In some the cases, metal ionsAll the results produced by ABScan can be visualized interactively on the web-server. Jmol Applet is used to visualize the contribution of residues towards ligand interaction .d3.js library has been utilized to plot the predicted ∆∆G values and subcomponents of the energetic scores reported by Autodock4 . An optiABS-Scan webserver can provide valuable insights on molecular recognition involving protein-ligand interactions. Experimentally determined protein-ligand structures can be studied to understand individual residue contributions towards ligand binding. Modeled complexes can also be submitted to infer the feasibility of the interaction. We believe that ABS-Scan would add one more dimension to the analysis of binding sites in proteins, comparison of various ligand interactions and be of importance to researchers performing ASM studies.http://proline.biochem.iisc.ernet.in/abscan/https://github.com/praveeniisc/ABS-Scanhttps://github.com/F1000Research/ABS-Scan/releases/tag/V1.0http://dx.doi.org/10.5281/zenodo.1280634ABS-Scan is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. No more comments. This version of the manuscript has been improved on several lines and the reply by the authors are acceptable. The paper is approved.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. In silico alanine scanning mutagenesis for binding site residues in protein–ligand complex", the authors have discussed the development of anin silico workflow for the prediction of important residues for ligand recognition in a given protein-ligand complex. In our opinion, the ABS-Scan tool has been designed and validated satisfactorily. The online web-server is intuitive, fast and easy to use. In the present version of the paper and the software tool, the authors have incorporated the recommendations provided by the previous referees, many of which in our opinion are relevant.In their manuscript entitled " ABS–Scan:The authors state that 288 of the total 343 protein-ligand complexes from the CSAR dataset, 135 of the 195 protein-ligand complexes from the PDBbind dataset and 54 of the 79 experimental datasets could be processes by ABS-Scan. Although, the authors mention in their responses to referee comments of version 1 of the manuscript, that ABS-Scan rejected some protein-ligand complexes due to “unusual atom types or missing protein/ligand atoms or unusual convention for ligand atoms”, it would probably be helpful for users to determine the kind of protein-ligand complexes that are suitable/unsuitable for prediction using ABS-Scan if authors could discuss this aspect in a sufficiently detailed manner. Was there anything in particular or common in the rejected complexes from the three datasets used for validation? It would be great if they could possibly provide the list of the rejected complexes and the reason for rejection, similar to what they have done for the protein-ligand complexes which were used for validation of the ABS-Scan tool.It is usually seen that short peptide ligands are present as a separate chain in the protein structure files. Particularly for these cases, there is no option in the web-interface of ABS-Scan to enter the other chain as ligand. Can you please elaborate how your software tool handles such protein-ligand complexes?While providing the ΔΔG values for the mutated residues, we think it would be a useful if the ABS-Scan server also provides some indication of evolutionary conservation of the residue. This would allow the users to translate ABS-Scan results from already known or docked protein-ligand complexes to other homologs of the same protein family.We have the following suggestions.We have read this submission. We believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. The authors have now  incorporated all the suggestions I made in my 1st review. In fact the revised web application is able to include the solvent molecules. I think that the revised version is now ready to be indexed in PubMed.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. This paper reports an attempt to develop an original tool that simulates alanine scanning mutagenesis to probe residues involved in the process of ligand recognition in proteins.More precisely, the work describes the development of a work flow that implements known methodologies for homology modeling of alanine single-point mutants of a protein and for molecular docking. Even though, this can be viewed as a methodological paper.http://proline.biochem.iisc.ernet.in/abscan/validation). It is more confusing because the results provided for the vitamin D receptor (PDB: 1IE9) is not about binding affinities but "translational activity". I'm here suggesting that detailed data for all mutations taken from all "79" entries are provided to the community in the form of a table or downloadable flat or excel-type file.The authors claim that they performed a "validation" of  their tool on a dataset that comprises "79 entries" carefully selected from PDB . Their evaluation is based on finding a correlation between docking scores with experimentally determined binding affinities. In their paper, the authors provide evidence of this validation by providing results of "Experimental correlation" for only one example  which relates to binding of rat 3-alpha-hydroxysteroid dehydrogenase (PDB: 1AFS) to testosterone and progesterone. Since they must have it, clearly, the authors should provide their evaluation of this correlation on all "79 entries". I would expect at least that they provide a new Figure 2 that comprises all data points coming from these "79 entries" to sustain their claim and help readers to evaluate the global performance of their tool. They attempted to provide few additional results on their website  that will be subjected to molecular docking.Regarding the alanine scanning procedure, there are issues regarding the treatment of alanine and proline. They should both be discarded from the alanine scanning protocol: alanine is already present in the structure while proline is not suitable for mutations because of the major protein backbone rearrangements that should be performed to properly mutate it.For such a tool, it is at stake to evaluate its performance using different homology modeling and molecular docking methods. The rational behind the choice of Modeler over other methods like Rosetta is not indicated. Likewise, the reason why Autodock and not Dock etc or even Autodock Vina is not explained.The efficiency of molecular docking using AutoDock is also dependent on the docking protocol used. In such an automated "screen", care should be taken about the preparation of the receptor, the ligand and the grid. For example, are the ligands kept flexible ? In the manuscript, there are no indications about how the authors dealt with this central issue. The authors are encouraged to describe precisely and discuss their docking protocol.According to the AutoDock 4.0 article, the median error range in energy estimation for any protein-ligand evaluation is 1.5-2.0 kcal/mol. In their study, the ∆∆G differences for ligand binding between mutant and native forms of the proteins are far below 2.0 kcal/mol. Thus, it is difficult to rank the mutants. Also, how the authors chose the 0.5 kcal/mol ∆∆G threshold is not clear. There is no discussion how this threshold compares with the intrinsic limits in precision of AutoDock.The definition of ligand in the tool is problematic. In case of oligo or polysaccharides, the carbohydrate residues are erroneously considered separately. For example, in the 1J84 entry from PDB, the carbohydrate-binding module (CBM) is bound to cellotretraose, a 1,4-β-D-glucan composed of four ß-D-glucose residues linked by ß-1,4 osidic linkages. When this PDB entry is submitted to ABS-Scan, it erroneously splits the oligomer into smaller entities that correspond to the chemical IDs of its constituents . This is a serious flaw in their software.http://salilab.org/MODELLER/wiki/Mutate_model) or AutoDock code (http://mgltools.scripps.edu/api/AutoDockTools/AutoDockTools.Utilities24.compute_AutoDock41_score-pysrc.html). While it is common to see people to reuse available codes, the authors do not properly cite the source of their codes they posted on Github and used for providing a complete service to the community: at least 80% of the “alanine_scanning.py” code comes from either MODELLER examples . Their evaluation is based on finding a correlation between docking scores with experimentally determined binding affinities. In their paper, the authors provide evidence of this validation by providing results of "Experimental correlation" for only one example which relates to binding of rat 3-alpha-hydroxysteroid dehydrogenase (PDB: 1AFS) to testosterone and progesterone. Since they must have it, clearly, the authors should provide their evaluation of this correlation on all "79 entries". I would expect at least that they provide a new Figure 2 that comprises all data points coming from these "79 entries" to sustain their claim and help readers to evaluate the global performance of their tool. They attempted to provide few additional results on their website . It is more confusing because the results provided for the vitamin D receptor (PDB: 1IE9) is not about binding affinities but "translational activity". I'm here suggesting that detailed data for all mutations taken from all "79" entries are provided to the community in the form of a table or downloadable flat or excel-type file.A suitable dataset for validation would be one that reports binding affinities for both wild-type and mutant proteins with same ligand, performed in a uniform experimental environment, for large number of proteins. Although such a dataset exists for protein-protein alanine scanning mutagenesis for eg., Rosetta alanine scanning), there are none reported for protein-ligand interactions.a or Kd values) are reported for some, changes in catalytic efficiencies are reported for some others. For some others, reporter assays are given which indicate capability of the downstream process more qualitatively. Hence it is difficult to perform a systematic comparison from these with the ∆∆G values calculated from our tool in this study. Nevertheless from this dataset, some examples were hand-picked, corresponding primary literature were read and known residue importances obtained, which were then compared with the predicted ones from our tool.  In any case, ABS-Scan analysis has been successfully performed on 54  complexes, which provide the extent of contribution to ligand binding of each residue in each  site, in the form of a ranked list of residue-wide ∆∆G values. All this information has been made available to the community, through our webserver -  .Since no such dataset was available to us, we systematically extracted PDB entries of ligand bound complexes and the corresponding binding sites in them that contained information about experimental alanine-scanning mutagenesis. However, the manner in which the effects of mutagenesis are reported in these differ significantly. While differences in ligand binding strengths  protein complexes with native ligands versus decoy ligands from and (b) list of well curated with precise binding site definitions for known protein-ligand complexes used for benchmarking docking algorithms. From both of these, ∆∆G scores are in the range of 0.5 was significant.et al., 2004, Huanget al., 2008). Of the 195, 135 could be processed successfully for preparation of the protein-ligand complexes for analysis. . a) A fresh dataset derived from PDB-Bind core dataset consisting of 195 protein-ligand complexes, which has been developed for the purposes of benchmarking docking algorithms (Kimb) A dataset of 343 protein-ligand complexes, each with a native and a decoy ligand. 288 structures out of 343 could be successfully evaluated. (Here again the others were omitted due to difficulties in automatic protein/ligand preparation).In the process, since ABSscan has been run for all these complexes, information about key contributing residues is generated for each of them. This has been made available through the webserver.  Residue-wise contribution is obtained and presented in a ranked order for each complex, thus providing a ready resource of important residues for ligand binding.http://proline.biochem.iisc.ernet.in/abscan/validationThe results of these can be accessed from the validation section on the webserver –The amount of independent PDB entries in their dataset is not 79. In fact, in some of PDB entries, multiple ligands were observed. Surprisingly, they consider these as separate entries. So their data is redundant with respect to the proteins. unique PDB entries’. In any case we refer to them now as ‘binding site entities’ to reflect this more clearly.These reflect independent binding sites (with bound ligands).  As can be expected, some proteins have multiple sites with different ligands, making it necessary to consider them separately. Hence 79 sites are unique and come from 46 PDB entries. In the original manuscript, the dataset of 79 was never meant to reflect ‘When generating homology models for protein variants, even if these are single point mutants, assessment of the quality of the models is a critical step. Selecting best models may not be that trivial. The authors need to clarify how they implement in their work flow the assessment of the quality of the models and consequently, what criteria they used for selecting the best models (and how many of them) that will be subjected to molecular docking.http://salilab.org/modeller/wiki/Mutate%20model). The initial restraints for generation of the model is derived from the wild-type structure itself. Assumptions necessary for modelling point mutations introduced through alanine-scanning mutagenesis protocol at the binding sites are that (a) they are unlikely to change the overall structure of the protein drastically and (b) the ligand moiety roughly retains the same conformation in comparison with the wild-type complex to interact with the mutated structure.Model quality has been considered as part of the modelling pipeline itself. Given the scale of the study, it is practical to generate one model for each mutant, but care is taken to ensure that it is optimal and free of errors in terms of bad contacts or atomic clashes. The optimization protocol used consists of 200 iterations of conjugate gradient, followed by molecular dynamic simulation for 4fs and simulated annealing with 200 iterations at different temperatures . Since modelling protocols have been well established for a long time now, we did not see the need for adding this information explicitly in the original MS.  In any case, based on the reviewers suggestion,  this information has been added to the revised version. Normalized DOPE scores are reported for both the native and mutant structures. DOPE refers to ‘Discrete Optimized Protein Energy’ and is a statistical potential which checks for the feasibility of the observed interactions. Protein structures with lower DOPE scores  can be considered to be of good quality  and roughly the same number of tools for protein-ligand docking (http://en.wikipedia.org/wiki/Docking_%28molecular%29). The precise reason for choosing ‘Modeller’ or ‘Autodock’ is perhaps because of our own experience in using these tools along with availability of extensive documentation, tutorials and ease of implementation.  Moreover, both these libraries had python bindings available and hence could be merged into a single script using python. In future, we plan to develop a pymol plugin for the same. The goal of our study is not to develop a modelling algorithm or a new parameter for building models. The most widely used tool for homology modelling – Modeller, which we have currently included in the workflow, has about 1500 citations. Currently there are more than 50 tools for homology modeling -.”According to the AutoDock 4.0 article, the median error range in energy estimation for any protein-ligand evaluation is 1.5-2.0 kcal/mol. In their study, the ∆∆G differences for ligand binding between mutant and native forms of the proteins are far below 2.0 kcal/mol. Thus, it is difficult to rank the mutants. Also, how the authors chose the 0.5 kcal/mol ∆∆G threshold is not clear. There is no discussion how this threshold compares with the intrinsic limits in precision of AutoDock.http://www.csardock.org/) was used to define a cut-off of 0.5. This has also been validated on PDBbind core dataset (http://www.pdbbind-cn.org/). Figures 3A and 3B have been added along with explanations in the manuscript.The median error range of the energy estimation reported in AutoDock 4.0 article is for the total ∆G score between the experimental and predicted values, whereas in this case it is for individual residue contributions. The distribution of the ∆∆G values obtained for the decoy and cognate ligands from the CSAR dataset  is bound to cellotretraose, a 1,4-β-D-glucan composed of four ß-D-glucose residues linked by ß-1,4 osidic linkages. When this PDB entry is submitted to ABS-Scan, it erroneously splits the oligomer into smaller entities that correspond to the chemical IDs of its constituents . This is a serious flaw in their software.etc.) also track ligands through such residue identifiers.This is not really a 'problem' and is an established work-around to avoid long computation and hence long waiting time for the user. All this does is to split peptides or oligosaccharides into individual moieties , as per the convention currently followed by PDB.  How can this be a ‘serious flaw’? It does not, in any manner, influence the results. Many other tools for protein-ligand interaction analysis such as LPC  or AutoDock code (http://mgltools.scripps.edu/api/AutoDockTools/AutoDockTools.Utilities24.compute_AutoDock41_score-pysrc.html).We have indeed already cited all the tools used in the manuscript to which source codes are linked. In any case, these references are now highlighted in the source code also. Both Autodock and Modeller are released under the GNU public license, making their source code freely usable to all interested parties.  Moreover, these are the primary source and codes are not extracted from any third-party tools. The purpose of putting it on Github is to be completely open about the details of the protocol and make our work fully accessible to anyone interested.   F1000Research, we submitted it as a software tool.We would again like to remind the reviewer here that source-code is used only by an advanced user. The reviewer may be aware of the time and effort involved in producing a web-application interface that is embedded with visualization features. This has been done with the belief that it will save precious time for researchers who do not have the expertise or the interest in installation and handling command-line interfaces for such tools. We initially proposed this as a web-tool, but since that section is no longer available in In silico alanine scanning mutagenesis for binding site residues in protein–ligand complex' reports development of a web server for performingin silico alanine scanning mutations for studying protein-small molecule interactions. It further validates the tool by taking a list of already published Alanine scanning data along with the X-ray crystallographic structures of the relevant protein-ligand complexes. ABS-Scan provides a user-friendly web interface and will be very much useful for experimentalists to assess the outcome of mutations designed to study protein-ligand  interactions. Overall the web tool is well explained in the manuscript.The manuscript 'ABS–Scan:My main concern is the method for energy calculations, authors used to predict the individual contribution upon mutation. It does not include waters and metals. As it is well established that many of the protein-ligand interactions are water mediated and therefore water plays very important role in determining the specificity as well as affinity. Authors could use energy function which includes waters and metals as well otherwise the current version could only be used for protein-ligand complexes in which water/metal atoms have been shown to play any role in stabilizing the ligand in the binding pocket.Thus in my opinion, I think it could be indexed with inclusion of an updated energy function. Alternatively, its sole applicability for protein-ligand interaction without involvement of solvent molecules should be mentioned in the conclusion.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. We thank the reviewer for going through our manuscript and finding the work useful. These are indeed valid points and have been addressed in the revised version. Bridge water molecules do play an important role in the protein-ligand interactions. One has to take into account the resolution of the protein structure to determine the confidence of the placed water molecules. Hence an advanced option is provided wherein these water molecules when present at the site can be considered to be a part of the corresponding ligand moiety. The user can upload his/her own pdbqt file for the ligand with the appropriate water molecules added to it.  An example of protein lysine methyltransferases complexed with S-adenosyl methionine has been described in the manuscript. The corresponding pqbqt file of the ligand can be downloaded from the example section. The results of these can also be accessed from example section of the web-server.


According to the statistics in the newest version of GOLD database (sf.org/) , 6651 gesb.org/)  has publsb.org/) , 4. How  pili by combining ambiguous symmetric constraints with sparse distance information obtained from experiments. The method was successfully implemented to the reconstruct the gonococcal (GC) pilus from Neisseria gonorrhoeae. A global sampling in a wide range implied that a pilus might have more than one but fewer than many possible intact conformations.In “Predicting Homogeneous Pilus Structure from Monomeric Data and Sparse Constraints,” K. Xiao et al. developed a new approach to predict pseudoatomic models ofIn “Evolutionary and Expression Analysis of miR-#-5p and miR-#-3p at the miRNAs/isomiRs Levels,” L. Guo et al. explored the potential evolutionary and expression divergence and relationships between miRNAs from different arms of different/same pre-miRNAs according to the arm selection and/or arm switching phenomenon in miRNA world. They found no bias in the numbers but different nucleotide compositions between 5p-miRNA and 3p-miRNA. IsomiR expression profiles from the two arms are always stable, but isomiR expressions in diseased samples are prone to show larger degree of dispersion. miR-#-5p and miR-#-3p have relative independent evolution/expression patterns and datasets of target mRNAs, which might also contribute to the phenomena of arm selection and/or arm switching. Simultaneously, miRNA/isomiR expression profiles may be regulated via arm selection and/or arm switching, and the dynamic miRNAome and isomiRome will adapt to functional and/or evolutionary pressures. A comprehensive analysis and further experimental study at the miRNA/isomiR levels are quite necessary for miRNA study.In “Strong Ligand-Protein Interactions Derived from Diffuse Ligand Interactions with Loose Binding Sites,” L. Marsh presented a fine-grained computational method for numerical integration of total binding free energy arising from diffuse regional interaction of a ligand in multiple conformations using a Markov Chain Monte Carlo. The application to the bacterial multidrug efflux pump AcrB indicated that diffuse binding effects could cause 100-fold binding affinity for some ligands while little role in the binding of other ligands. Analysis of other proteins with large binding pockets indicated that the influence of this process varies greatly, dependent on ligand and protein target. This work may be of broad interest to those studying a variety of biological systems including protein folding, DNA-protein binding, and drug-receptor docking that depend on dispersed interactions.γ Receptor 1 as a Model,” J. Černý et al. presented a new strategy for designing high affinity variants of a binding protein through mutating residues at positions lining internal cavities of one of the interacting molecules instead of at the interface. The test on interferon-γ receptor 1 has brought up to sevenfold increase in the binding affinity. Analysis shows that the affinity increase is linked to the restriction of molecular fluctuations in the unbound state of the receptor. This serves as an example of a viable strategy for designing protein variants with increased affinity.In “Redesigning Protein Cavities as a Strategy for Increasing Affinity in Protein-Protein Interaction: Interferon-In “Multi-Instance Multilabel Learning with Weak-Label for Predicting Protein Function in Electricigens,” J.-S. Wu et al. have applied the state-of-the-art MIML with weak-label learning algorithm MIMLwel for predicting protein functions in two typical real-world electricigens organisms widely used in microbial fuel cells studies. The experimental results validate the effectiveness of MIMLwel algorithm in predicting protein functions with incomplete annotation.In “Detecting Protein-Protein Interactions with a Novel Matrix-Based Protein Sequence Representation and Support Vector Machines,” Z.-H. You et al. developed a novel computational approach to effectively detect the protein interactions based on a novel matrix-based representation of protein sequence by SVM. The sequence information includes the order of amino acids and composition of dipeptide. The prediction was proven highly accurate on the benchmark of yeast PPIs datasets. Thus, it can be a helpful supplement to the missing data and false positive by experimental results from high-throughput techniques.In “An Improved Method for Completely Uncertain Biological Network Alignment,” B. Shen et al. designed an improved method to analyze uncertain biological networks by complete probabilistic biological network alignment. This method has solved the weakness of PBNA by allowing both networks to be probabilistic; thus, it could take full advantage of the uncertain information of biological network. The new method was proven to consistently improve over PBNA in both GO Consistency and Global Network Alignment Score.In “A Systematic Analysis of Candidate Genes Associated with Nicotine Addiction,” M. Liu et al. have performed a systematic analysis on a set of nicotine addiction-related genes (NAGenes) to explore their characteristics at network levels. They found that NAGenes tended to have a more moderate degree, weaker clustering coefficient in the network, and are less central in the network compared to genes related to alcohol addiction or cancer. In addition, an intuitional view was provided to understand their major molecular functions by six clusters from the clustering of these genes with themes in synaptic transmission, signal transduction, metabolic process, and apoptosis. Moreover, it was found that nicotine addiction involves neurodevelopment, neurotransmission activity, and metabolism related biological functions. The systematic network and functional enrichment analysis for nicotine addiction in this study is valuable for understanding the molecular mechanisms underlying nicotine addiction.In “Constraint Programming based Biomarker Optimization,” M. Zhou et al. proposed an algorithm for highly accurate feature selection for biomedical classification problem, while allowing the inclusion of user-input constraints for the optimization process. The experimental results showed that the proposed method provided flexibility of allowing both the well-known disease biomarkers like P53 and the existing feature selection algorithms' results as the constraints, while achieving promising classification performances. This work provided useful tool for efficiently exploring the health big data, consisting of both the bio-OMIC data and huge amount of knowledge accumulated in the literature and other sources.In “An Improved Opposition-Based Learning Particle Swarm Optimization for the Detection of SNP-SNP Interactions,” J. Shang et al. presented an improved method for detecting SNP-SNP interactions by using opposition-based learning particle swarm optimization. The algorithm has ensured the ability of global searching and prevented premature convergence. The application to a dataset of age-related macular degeneration shows the strength of the method on real applications by capturing important features of genetic architecture not previously discovered. The method provides an important tool in detecting SNP-SNP interactions in future.In “Nucleosome Organization around Pseudogenes in the Human Genome,” G. Liu et al. investigated the effect of nucleosome positioning on pseudogene transcription. The results show that, for transcribed pseudogenes, nucleosomes upstream of the start positions and end positions of transcribed pseudogenes are depleted. Interestingly, the same depletion is also observed for nontranscribed pseudogenes. The consistent pattern of sequence-dependent prediction with the assessment by experimental data indicates that sequence-dependent mechanism of nucleosome positioning may play important roles in both the transcription initiation and termination of pseudogenes.