bluuebunny/crossref_metadata_2025 huggingface dataset:
Vector-G: Multi-Modular SVM-Based Heterotrimeric G Protein	Prediction
Authors: Jain Preti , Wadhwa Puneet , Aygun Ramazan , Podila Gopi
Published: January 2025

Heterotrimeric G proteins interact with G protein-coupled receptors in response to stimulation by hormones, neurotransmitters, chemokines, and sensory signals to intracellular signaling cascades. Recently reported studies indicate that G protein subunits play a significant role in different eukaryotic diseases including inflammation, neurological diseases, cardiovascular diseases, endocrine disorders as well as plant pathogen response, infectious hyphae growth, differentiation and virulence of pathogenic fungi. Thus a study of their functions, signaling pathways, and protein interactions may lead to the development of various preventive approaches. The diversity of α, β and γ subunits of G proteins necessitates a prediction algorithm that helps in the identification of new proteins such as Gβ where WD-40 repeats are not well characterized. The currently available techniques for finding G proteins are homology based search analyses and wet lab experiments, which are not very effective in finding new classes of proteins. We present here a robust computational method for finding new G proteins and their homologs using a SVM based pattern recognition algorithm. Several physicochemical and compositional properties including dipeptide, tripeptide and hydrophobicity composition are used for generating the SVM classifiers. This method has 96.17%, 95.38%, 97.6% sensitivity and 99.45%, 100%, 100% specificity on test sets for G protein α, β, and γ subunits, respectively. This algorithm correctly predicts the known α, β and γ subunits reported in literature. One important contribution of this algorithm is that it helps in improving genome annotation of several proteins as G proteins and serves as a useful tool for comparative genomic analysis of G proteins. Using this method, novel G protein subunits are predicted in 31 genomes covering plant, fungi and animal kingdom. The software is available at the website http://biomine.cs.uah.edu/bioinformatics/svm_prog/scripts/GProteins/vectorg.html. Supplementary files: The supplementary files are available on http://www.bioinfo.de/isb/2008/08/0013/supplementary_material/.
In silico Comparison of Real-Time PCR Probes	for Detection of Pathogens
Authors: Ram Siya , Singh Ram Lakhan , Shanker Rishi
Published: January 2025

Rapid diagnostics and risk assessment of the pathogens is possible by Real-Time Polymerase Chain Reaction (PCR) probes like TaqMan, Molecular Beacon (MB) and FRET. However, validation of such probes for real-life samples is an expensive and time consuming proposition. Hence, development and comparison of real-time probes in silico can be the first step in selection of most appropriate probe chemistry. The virulence genes specific for a model pathogen, Escherichia coli O157:H7, transmitted worldwide by contaminated water and food, were chosen to compare probe chemistries. MB was observed to be the best probe chemistry for virulence genes stx1, stx2 and eae, while FRET was preferred for hlyA gene, based on T $_{m}$ and free energy values for self-dimer, hairpin and cross-dimer. Secondary structure analysis indicated that MB design was flexible and less dependent on nucleotide arrangement and repetitive sequences in the genes compared to TaqMan and FRET probes. In addition, multiplexed MB probes could be a feasible option using a single non-fluorescent quencher for high throughput diagnostics.
Structure and Mechanism of a Transmission Blocking Vaccine	Candidate Protein Pfs25 from P. falciparum: A Molecular	Modeling and Docking Study
Authors: Sharma Babita
Published: January 2025

Malaria is caused by the protozoan Plasmodium. The parasite Plasmodium completes its life cycle inside two hosts, i.e. human and mosquito. Among all known Plasmodium species, Plasmodium falciparum is known to cause maximum mortality. Various studies done on the mosquito stages of the parasite suggest that the proteins present on the parasite's surface are responsible for its survival under the adverse conditions prevailing in the mosquito midgut. When human blood containing Plasmodium gametocytes enters the mosquito gut, the gametocytes form gametes which then fuse to form zygotes. At this stage two closely related proteins, Pfs25 and Pfs28 are expressed on the surface of the parasite which continue to express up to the young oocyst stage. These proteins present on zygotes, ookinetes and young oocysts of Plasmodium are categorized in P25 and P28 families and are well known malaria vaccine candidate proteins. In this study, we have done sequence analysis, homology modeling and docking studies of a typical member of the P25 family of ookinete surface protein, i.e. Pfs25 from Plasmodium falciparum. We have built a 3D model of Pfs25 based on the X-ray crystallographic structure of Pvs25 from Plasmodium vivax. Also we have modeled the Fv region of the malaria transmission blocking monoclonal antibody 4B7. This antibody is the transmission blocking monoclonal antibody for Pfs25 protein. Pfs25 and 4B7 scFv (single chain variable fragment only) docking results indicate that EGF domain III of the Pfs25 protein interacts with the scFv region of modeled 4B7 antibody forming seven hydrogen bonds out of which six are formed with heavy chain of scFv region. Docking results of Pfs25 with gamma chain of laminin also suggest a possible role of Pfs25 protein in host parasite interaction.
A Steady State Model for the Transcriptional Regulation of	Filamentous Growth in Saccharomyces cerevisiae
Authors: Unni Vinod Palakkad Krishnan , Venkatesh Kareenhalli V.
Published: January 2025

Occurrence of multiple upstream activation sites (UASs) is a structural motif that is observed within the promoter of eukaryotic genes for coordinating gene expression. Transcriptional activation depends on the ability of transcriptional activators to bind to its specific UASs, which are kept inaccessible due to the nucleosomal organization of the chromatin. Targeting of chromatin remodeling complexes by a sequence specific transcriptional activator is shown to be detrimental for transcriptional initiation. Here, we analyze such a regulatory structure involving ordered recruitment of transcriptional activators and chromatin remodeling complexes with respect to activation of a flocculin gene, FLO11 involved in the filamentous growth to gain insights into its regulation. We develop a steady state model for the transcriptional regulation of FLO11 by primary transcriptional activators Flo8p, Ste12p, Tec1p and Mss11p, which are under a complex network comprising of cAMP and MAPK pathways. Our analysis predicts that the FLO11 promoter should undergo varying chromatin remodeling activity from partial to complete disassembly depending upon the concentration of Ste12p. This variation should be sensitive and sharply shift to saturate with Ste12p concentration. Overexpression of Ste12p can increase the overall chromatin remodeling activity by increasing the local concentration of remodeling complex through active recruitment. Further, we demonstrate that the chromatin remodeling activity brings about amplification of cAMP and MAPK signal and in absence of either of the signals, the input signal required for the other increases. We also discuss the results obtained from our steady state analysis in respect to other eukaryotic genes.
PRIMe: A Web Site That Assembles Tools for Metabolomics and	Transcriptomics
Authors: Akiyama Kenji , Chikayama Eisuke , Yuasa Hiroaki , Shimada Yukihisa , Tohge Takayuki , Shinozaki Kazuo , Hirai Masami Yokota , Sakurai Tetsuya , Kikuchi Jun , Saito Kazuki
Published: January 2025

PRIMe (http://prime.psc.riken.jp/), the Platform for RIKEN Metabolomics, is a Web site that has been designed and implemented to support research and analysis workflows ranging from metabolome to transcriptome analysis. The site provides access to a growing collection of standardized measurements of metabolites obtained by using NMR, GC-MS, LC-MS, and CE-MS, and metabolomics tools that support related analyses (SpinAssign for the identification of metabolites by means of NMR, KNApSAcK for searches within metabolite databases). In addition, the transcriptomics tools provide Correlated Gene Search, and Cluster Cutting for the analysis of mRNA expression. Use of the tools and database can contribute to the analysis of biological events at the levels of metabolites and gene expression, and we describe one example of such an analysis for Arabidopsis thaliana using the batch-learning self-organizing map (BL-SOM), which is provided via the Web site.
Sequence and Structural Analyses of Interleukin-8-Like Chemokine	Superfamily
Authors: Kanagarajadurai Karuppiah , Sowdhamini Ramanathan
Published: January 2025

Interleukin-8 and related chemokines are small proteins that bind to receptors belonging to the large family of G-protein-coupled receptors. They can cause migration of cells like neutrophils and eosinophils and some of them are implicated in angiogenic diseases. More than 40 subfamilies of these ligands are known that share poor sequence similarity and display receptor specificity. There is very little structural information about the mode of binding between ligands and the receptors. We have employed multi-fold sensitive sequence search methods to provide a repertoire of 252 putative interleukin-8 proteins and homologues, which are shared across humans, aves and fish. The sequences can be organized into five major known clusters. The propensity of occurrence of certain amino acid alphabets is found to be specific in different locations of the polypeptide fold. The sequence dispersion is also observed to be cluster-specific when examined by Evolutionary Trace procedure. Amino acid alphabet analysis and Evolutionary Trace procedure reveal cluster-specific amino acid distribution that provide clues about how the small fold of the ligand could display remarkable receptor specificity. We notice regions, like the β $_{1}$ -β $_{2}$ loop of the fold, that are potentially involved in receptor recognition and specificity that could be potential sites for residue mutations. Systematic studies of the distribution patterns enable better understanding of the evolution and molecular recognition of this important and diverse protein superfamily.
Comparative Studies on Inhibitors of HIV Protease: A Target for	Drug Design
Authors: Jayaraman Sudha , Shah Kavita
Published: January 2025

Bioinformatics tools are employed lately for in silico structure-function analysis of proteins. HIV protease inhibitors nelfinavir and tipranavir belong to the extended multi-ring systems. The intermolecular interactions made by the functional groups of the different residues on the protein molecule are probed with the help of computational tools for protein homology studies so as to identify the functional residues, create single, double, triple mutations using, different bioinformatics servers and to observe the changes brought therein by docking. BLAST, RosettaDesign, PatchDock, Chimera were used in the present study for identifying the inhibitors as better drug targets. The HIV protease-nelfinavir complex (PDB code: 1OHR) and HIV protease V82F/I84V double mutant-tipranavir complex (PDB code: 1D4S) were used as templates for introducing mutations on the HIV protease active site. In this study a structure-based computer-assisted search for the comparison of the two inhibitors of HIV protease was carried out. The results suggest that the two inhibitors nelfinavir and tipranavir could be used for treatment of AIDS by targeting the enzyme HIV protease as neither of the two inhibitors exhibit any cross-reactivity with other human proteins, they readily bind to the mutated enzyme active site and still remain linked with the enzyme-substrate complex in the presence of water molecules. The inhibitor nelfinavir undergoes several changes in hydrogen bonds formation with the introduction of mutations on the HIV protease active site. It either has a positive or a negative inhibitory effect on HIV protease and forms new hydrogen bonds with a shorter bond lengths. Nelfinavir also seems to be an inhibitor of a more narrow specificity as it shows changes in binding bringing thereby conformational changes in the native enzyme. Tipranavir on the other hand seems to be a broad specificity inhibitor as no changes in the bond lengths with the introduction of mutations are observed. Of the two inhibitors tipranavir could be targeted more effectively for designing future drug analogues as it is less vulnerable to mutations. The HIV mutants reported herein could also be used for preliminary identification of specific inhibitors as drugs that may alter the HIV protease activity for medicinal use.
Identification of Differentially Expressed Genes by Meta-Analysis	of Microarray Data on Breast Cancer
Authors: Kondrakhin Yury V. , Sharipov Ruslan N. , Kel Alexander E. , Kolpakov Fedor A.
Published: January 2025

Albeit the great number of microarray data available on breast cancer, reliable identification of genes associated with breast cancer development remains a challenge. The aim of this work was to develop a novel method of meta-analysis for the identification of differentially expressed genes integrating results of several independent microarray experiments.

We developed a statistical method for identification of up- and down-regulated genes to perform meta-analysis. The method takes advantage of hypergeometric and binomial distributions. Using our method we performed meta-analysis of five data sets from independent cDNA-microarray experiments on breast cancer. The meta-analysis revealed that 3.2% and 2.8% of the 24,726 analyzed genes are significantly (P-value < 0.01) down- and up-regulated, respectively. We also show that properly applied meta-analysis is a good tool for comparison of different breast cancer subtypes. Our meta-analysis showed that the expression of the majority of genes does not show significant differences in different subtypes of breast cancer.

Here, we report the rationale, development and application of meta-analysis that enable us to identify biologically meaningful features of breast cancer. The algorithm we propose for the meta-analysis can reveal the features specific to the breast cancer subtypes and those common to breast cancer. The results allow us to revise the previously generated lists of genes associated with breast cancer and also identify most promising anticancer drug-target genes.
Molecular Modelling Study of the 3D Structure of the Bovine Viral	Diarrhea Virus (BVDV) Helicase
Authors: Brancale Andrea , Vlachaki Chrisanthy , Vlachakis Dimitrios
Published: January 2025

Bovine viral diarrhea virus (BVDV) is a member of the Flaviviridae family of viruses and constitutes a very important pathogen for livestock around the world [1]. The viral helicase is an enzyme essential for the proliferation and transmission of the virus. In this work a 3D-model of the BVDV helicase was produced using homology modelling techniques and the known 3D-structure of the hepatitis C helicase of the Flaviviridae family as template, in an attempt to provide the means for structure-based design of novel anti-BVDV agents.
Molecular Modeling and Docking Studies of Glutamate Racemase in	Vibrio vulnificus CMCP6
Authors: Vidya Natarajan , Vadivukkarasi Bagavathkumar , Manivannan Gopalakrishnan , Anbarasu Krishnan
Published: January 2025

Identification of novel drug targets in silico in Vibrio vulnificus is important as it is one of the emerging pathogenic microorganisms. Glutamate racemase, an important constituent of bacterial cell wall is chosen for structure prediction using homology modeling. With the aid of tools and softwares like MODELLER and Swiss-PdbViewer, the 3D structure is predicted and the final model is refined by energy minimization. The quality of the refined model is assessed using PROCHECK. The interaction between the predicted structure of glutamate racemase and its potential inhibitors namely L-serine O-sulfate, (2R,4R)-2-amino-4-(2-benzo[b]thienyl)methyl pentanedioic acid, aziridino glutamate, exiguaquinol, γ-2 naphthylmethyl-D-glutamate and D-glutamine is analysed in silico by Autodock. The results indicate that certain residues like Asp13, Tyr45, Gly46, Asn78, Thr79, Cys185, His187 are highly conserved across the active site stretches of different bacterial species and may possibly assume precedence over the other residues for inhibitory action. This study provides an insight into the structure of glutamate racemase in V. vulnificus and also gives an idea about potential sites responsible for inhibitory action that could further be substantiated by experimental investigations.
Essential Amino Acids for the Stability of Human	Butyrylcholinesterase as Predicted by CUPSAT Server
Authors: Thomas Biju , Boopathy Rathanam
Published: January 2025

Although butyrylcholinesterase (BChE) is ubiquitous in the human system, its physiological function has often been questioned. Yet BChE has been the subject of active research since its discovery six decades ago. From an evolutionary view point, its stabilized existence even in absence of physiological function spared it from extinction. Herein, we present results on the amino acid residues essential for its stability using CUPSAT server. Out of the 523 amino acids analyzed, 168 were identified to be vital for stability of the protein, and further categorized into three classes. The first class has 82 sites wherein any amino acid substitution results in instability. The second class has 52 sites where on one amino acid substitution alone stability is observed, while the third class has 34 sites where even on two different substitutions, stability is maintained. These sites are distributed throughout the different categories of protein secondary structure, with the order of priority: random coils, α-helices and β-strands. Multiple sequence alignment of BChE showed that most of these residues (85.1%) are within the conserved region, while the few that are not at the conserved region are proposed to be unique residues (14.9%) to the protein which do not show identity with other known homologs. Moreover, majority of the amino acid residues in the functional regions of human BChE do not show instability upon point mutation, indicative of the residue-wise separation for stability and function. Specifically, the active site S198 on mutation does not show instability, while L318P is highly unstable. In addition, S53, G212 and W430 are not involved in its stability and in fact the protein is stable even with all other 19 substitutions. In conclusion, this study bodes well as a forerunner for mutation and protein engineering studies of BChE.
GO for Integration of Expression Data
Authors: Dotan-Cohen Dikla , Moonshine Dana , Natan Moshe , Shemer-Avni Yonat , Melkman Avraham A.
Published: January 2025

The low reproducibility of differential expression of individual genes in microarray experiments has led to the suggestion that experiments be analyzed in terms of gene characteristics, such as GO categories or pathways, in order to enhance the robustness of the results. An implicit assumption of this approach is that the different experiments in effect randomly sample the genes participating in an active process. We argue that by the same rationale it is possible to perform this higher-level analysis on the aggregation of genes that are differentially-expressed in different expression-based studies, even if the experiments used different platforms. The aggregation increases the reliability of the results, it has the potential for uncovering signals that are liable to escape detection in the individual experiments, and it enables a more thorough mining of the ever more plentiful microarray data.

We present here a proof-of-concept study of these ideas, using ten studies describing the changes in expression profiles of human host genes in response to infection by Retroviridae or Herpesviridae viral families. We supply a tool (accessible at www.cs.bgu.ac.il/∼waytogo) which enables the user to learn about genes and processes of interest in this study.
Molecular modeling and docking analysis of Beta-lactamases with inhibitors: A comparative study
Authors: Danishuddin Mohd , Khan Asad U.
Published: January 2025

Beta-lactamases are bacterial enzymes which impart resistance against β-lactam-antibiotics. CTX-Ms are the β-lactamases that target cephalosporin antibiotics (e.g. cefotaxime and ceftazidime) while SME-1, KPC-2, IMI-1 and SFC-1 target carbapenems. Clavulanic acid, sulbactam and tazobactam are traditional β-lactamase inhibitors while LN1-255 and NXL-104 whereas novel inhibitors, inhibiting the activity of these enzymes. Studying the binding pattern of these drugs is helpful in predicting the versatile inhibitors for betalactamases. The aims of the study were: describing the mode of interaction of CTX-M (modeled from the blaCTX-M gene of this study) and the said carbapenemases with their respective target drugs and inhibitors and to perform an in silico comparison of the efficacies of traditional and novel β-lactamase-inhibitors based on fitness score. The blaCTX-M marker was PCR-amplified from plasmid DNA of E. coli strain isolated from community-acquired urinary tract infection. E. coli C600 cells (harboring cloned blaCTX-M) were found positive for extended-spectrum-β-lactamase (ESBL) production by the double-disk-synergy test. The three dimensional structures of CTX-M-15, SME-1 and IMI-1 were predicted by Swiss Model Server. The interaction between selected structures and inhibitors was performed by GOLD 5.0. On the basis of the docking score and binding pattern, we conclude that compound LN1-255 followed by tazobactam is best inhibitor against all the selected target enzymes as compared to clavulanate, sulbactam and NXL-104. Five conserved amino acids, Ser70, Ser130, Lys235, Thr236 and Gly237 were found crucial in stabilizing the complexes through hydrogen bonding and hydrophobic interactions.
Coordinated evolution of the Hepatitis B Virus Polymerase
Authors: Campo D.S. , Dimitrova Z. , Lara J. , Purdy M. , Thai H. , Ramachandran S. , Ganova-Raeva L. , Zhai X. , Forbi J.C. , Teo C.G. , Khudyakov Y.
Published: January 2025

The detection of compensatory mutations that abrogate negative fitness effects of drug-resistance and vaccine-escape mutations indicates the important role of epistatic connectivity in evolution of viruses, especially under the strong selection pressures. Mapping of epistatic connectivity in the form of coordinated substitutions should help to characterize molecular mechanisms shaping viral evolution and provides a tool for the development of novel anti-viral drugs and vaccines. We analyzed coordinated variation among amino acid sites in 370 the hepatitis B virus (HBV) polymerase sequences using Bayesian networks. Among the HBV polymerase domains the spacer domain separating terminal protein from the reverse-transcriptase domain, showed the highest network centrality. Coordinated substitutions preserve the hydrophobicity and charge of Spacer. Maximum likelihood estimates of codon selection showed that Spacer contains the highest number of positively selected sites. Identification of 67% of the domain lacking an ordered structure suggests that Spacer belongs to the class of intrinsically disordered domains and proteins whose crucial functional role in the regulation of transcription, translation and cellular signal transduction has only recently been recognized. Spacer plays a central role in the epistatic network associating substitutions across the HBV genome, including those conferring viral virulence, drug resistance and vaccine escape. The data suggest that Spacer is extensively involved in coordination of HBV evolution.
Mixture model analysis reflecting dynamics of the population diversity of 2009 pandemic H1N1 influenza virus
Authors: Long Li-Ping , Yuan Changhe , Cai Zhipeng , Xu Huiping , Wan Xiu-Feng
Published: January 2025

Influenza A viruses have been responsible for large losses of lives around the world and continue to present a great public health challenge. In April 2009, a novel swine-origin H1N1 virus emerged in North America and caused the first pandemic of the 21st century. Toward the end of 2009, two waves of outbreaks occurred, and then the disease moderated. It will be critical to understand how this novel pandemic virus invaded and adapted to a human population. To understand the molecular dynamics and evolution in this pandemic H1N1 virus, we applied an Expectation-Maximization algorithm to estimate the Gaussian mixture in the genetic population of the hemagglutinin (HA) gene of these H1N1 viruses from April of 2009 to January of 2010 and compared them with the viruses that cause seasonal H1N1 influenza. Our results show that, after it was introduced to human population, the 2009 H1N1 viral HA gene changed its population structure from a single Gaussian distribution to two major Gaussian distributions. The breadths of HA genetic diversity of 2009 H1N1 virus also increased from the first wave to the second wave of this pandemic. Phylogenetic analyses demonstrated that only certain HA sublineages of 2009 H1N1 viruses were able to circulate throughout the pandemic period. In contrast, the influenza HA population structure of seasonal H1N1 virus was relatively stable, and the breadth of HA genetic diversity within a single season population remained similar. This study revealed an evolutionary mechanism for a novel pandemic virus. After the virus is introduced to human population, the influenza virus would expand their molecular diversity through both random mutations (genetic drift) and selections. Eventually, multiple levels of hierarchical Gaussian distributions will replace the earlier single distribution. An evolutionary model for pandemic H1N1 influenza A virus was proposed and demonstrated with a simulation.
Reconstructing viral quasispecies from NGS amplicon reads
Authors: Mancuso Nicholas , Tork Bassam , Skums Pavel , Ganova-Raeva Lilia , Măndoiu Ion , Zelikovsky Alex
Published: January 2025

This paper addresses the problem of reconstructing viral quasispecies from next-generation sequencing reads obtained from amplicons (i.e., reads generated from predefined amplified overlapping regions). We compare the parsimonious and likelihood models for this problem and propose several novel assembling algorithms. The proposed methods have been validated on simulated error-free HCV and real HBV amplicon reads. The new algorithms have been shown to outperform the method of Prosperi et. al [24]. Our experiments also show that viral quasispecies can be reconstructed in most cases more accurately from amplicon reads rather than shotgun reads. All algorithms have been implemented and made available at https://bitbucket.org/nmancuso/bioa/.
Numerical detection, measuring and analysis of differential interferon resistance for individual HCV intra-host variants and its influence on the therapy response
Authors: Skums Pavel , Campo David S. , Dimitrova Zoya , Vaughan Gilberto , Lau Daryl T. , Khudyakov Yury
Published: January 2025

Hepatitis C virus (HCV) is a major cause of liver disease world-wide. Current interferon and ribavirin (IFN/RBV) therapy is effective in 50%–60% of patients. HCV exists in infected patients as a large viral population of intra-host variants (quasispecies), which may be differentially resistant to interferon treatment. We present a method for measuring differential interferon resistance of HCV quasispecies based on mathematical modeling and analysis of HCV population dynamics during the first hours of interferon therapy. The mathematical models showed that individual intra-host HCV variants have a wide range of resistance to IFN treatment in each patient. Analysis of differential IFN resistance among intra-host HCV variants allows for accurate prediction of response to IFN therapy. The models strongly suggest that resistance to interferon may vary broadly among closely related variants in infected hosts and therapy outcome may be defined by a single or a few variants irrespective of their frequency in the intra-host HCV population before treatment.
Prediction of Neurotoxins Based on Their Function and Source
Authors: Saha Sudipto , Raghava Gajendra P.S.
Published: January 2025

We have developed a method NTXpred for predicting neurotoxins and classifying them based on their function and origin. The dataset used in this study consists of 582 non-redundant, experimentally annotated neurotoxins obtained from Swiss-Prot. A number of modules have been developed for predicting neurotoxins using residue composition based on feed-forwarded neural network (FNN), recurrent neural network (RNN), support vector machine (SVM) and achieved maximum accuracy of 84.19%, 92.75%, 97.72% respectively. In addition, SVM modules have been developed for classifying neurotoxins based on their source (e.g., eubacteria, cnidarians, molluscs, arthropods have been and chordate) using amino acid composition and dipeptide composition and achieved maximum overall accuracy of 78.94% and 88.07% respectively. The overall accuracy increased to 92.10%, when the evolutionary information obtained from PSI-BLAST was combined with SVM module of source classification. We have also developed SVM modules for classifying neurotoxins based on functions using amino acid, dipeptide composition and achieved overall accuracy of 83.11%, 91.10% respectively. The overall accuracy of function classification improved to 95.11%, when PSI-BLAST output was combined with SVM module. All the modules developed in this study were evaluated using five-fold cross-validation technique. The NTXpred is available at www.imtech.res.in/raghava/ntxpred/ and mirror site at http://bioinformatics.uams.edu/mirror/ntxpred.
Genome-wide Prediction and Annotation of Burkholderia Pseudomallei	AraC/XylS Family Transcription Regulator
Authors: Lim Boon-San , Chong Chan-Eng , Zamrod Zulkeflie , Nathan Sheila , Mohamed Rahmah
Published: January 2025

Many members of the AraC/XylS family transcription regulator have been proven to play a critical role in regulating bacterial virulence factors in response to environmental stress. By using the Hidden Markov Model (HMM) profile built from the alignment of a 99 amino acid conserved domain sequence of 273 AraC/XylS family transcription regulators, we detected a total of 45 AraC/XylS family transcription regulators in the genome of the Gram-negative pathogen, Burkholderia pseudomallei. Further in silico analysis of each detected AraC/XylS family transcription regulatory protein and its neighboring genes allowed us to make a first-order guess on the role of some of these transcription regulators in regulating important virulence factors such as those involved in three type III secretion systems and biosynthesis of pyochelin, exopolysaccharide (EPS) and phospholipase C. This paper has demonstrated an efficient and systematic genome-wide scale prediction of the AraC/XylS family that can be applied to other protein families.
BRM-Parser: A Tool for Comprehensive Analysis of BLAST and	RepeatMasker Results
Authors: Bajpai Anjali , Sridhar Settu , Reddy Hemakumar M. , Jesudasan Rachel A.
Published: January 2025

BLAST and Repeat Masker Parser (BRM-Parser) is a service that provides users a unified platform for easy analysis of relatively large outputs of BLAST (Basic Local Alignment Search Tool) and RepeatMasker programs. BLAST Summary feature of BRM-Parser summarizes BLAST outputs, which can be filtered using user defined thresholds for hit length, percentage identity and E-value and can be sorted by query or subject coordinates and length of the hit. It also provides a tool that merges BLAST hits which satisfy user-defined criteria for hit length and gap between hits. The RepeatMasker Summary feature uses the RepeatMasker alignment as an input file and calculates the frequency and proportion of mutations in copies of repeat elements, as identified by the RepeatMasker. Both features can be run through a GUI or can be executed via command line using the standalone version.
BTXpred: Prediction of Bacterial Toxins
Authors: Saha Sudipto , Raghava Gajendra P.S.
Published: January 2025

This paper describes a method developed for predicting bacterial toxins from their amino acid sequences. All the modules, developed in this study, were trained and tested on a non-redundant dataset of 150 bacterial toxins that included 77 exotoxins and 73 endotoxins. Firstly, support vector machines (SVM) based modules were developed for predicting the bacterial toxins using amino acids and dipeptides composition and achieved an accuracy of 96.07% and 92.50%, respectively. Secondly, SVM based modules were developed for discriminating entotoxins and exotoxins, using amino acids and dipeptides composition and achieved an accuracy of 95.71% and 92.86%, respectively. In addition, modules have been developed for classifying the exotoxins (e.g. activate adenylate cyclase, activate guanylate cyclase, neurotoxins) using hidden Markov models (HMM), PSI-BLAST and a combination of the two and achieved overall accuracy of 95.75%, 97.87% and 100%, respectively. Based on the above study, a web server called 'BTXpred' has been developed, which is available at http://www.imtech.res.in/raghava/btxpred/.

Supplementary information is available at http://www.imtech.res.in/raghava/btxpred/supplementary.html.
Differential Genome Analyses of Metabolic Enzymes in	Pseudomonas aeruginosa for Drug Target Identification
Authors: Perumal Deepak , Lim Chu Sing , Sakharkar Kishore R. , Sakharkar Meena K.
Published: January 2025

Complete genome sequences of several pathogenic bacteria have been determined, and many more such projects are currently under way. While these data potentially contain all the determinants of host-pathogen interactions and possible drug targets, computational tools for selecting suitable candidates for further experimental analyses are currently limited. Detection of bacterial genes that are non-homologous to human genes, and are essential for the survival of the pathogen represents a promising means of identifying novel drug targets. We used a differential pathway analyses approach (based on KEGG data) to identify essential genes from Pseudomonas aeruginosa. Our approach identified 214 unique enzymes in P. aeruginosa that may be potential drug targets and can be considered for rational drug design. About 40% of these putative targets have been reported as essential by transposon mutagenesis data elsewhere. Homology model for one of the proteins (LpxC) is presented as a case study and can be explored for in silico docking with suitable inhibitors. This approach is a step towards facilitating the search for new antibiotics.
The CAI Analyser Package: Inferring Gene Expressivity from Raw	Genomic Data
Authors: Ramazzotti Matteo , Brilli Matteo , Fani Renato , Manao Giampaolo , Degl'Innocenti Donatella
Published: January 2025

The Codon Adaptation Index (CAI) was introduced by Sharp and Li in 1987 to quantify codon usage similarities between a coding sequence and a set of reference sequences. When synonymous codons for a given amino acid exist, highly expressed genes seem to prefer some of them, according to tRNA abundance and thermodynamic issues. Some authors have described CAI-based methods to derive expressivity measures for all genes in a genome, in a computational framework.

Here we present the CAIAP (CAI Analyser Package), a platform independent package of computer programs allowing the calculation of the CAI and a deep study of gene expressivity from raw gene sequences. Our approach implements and optimizes a procedure to derive the reference sequences from whole genomes and use their codon usage for CAI estimation. Moreover, a set of analysis tools are provided to perform statistical analyses and therefore to give robustness to results. Objective: Our efforts were aimed to produce an easy-to-use and fully automatic set of programs specifically designed for the analysis of gene expressivity and inter-species comparisons on a great number of genomes. Moreover, the output integrates information coming from functional annotations of genes.

We are maintaining a web server storing our analyses for hundreds of genomes, allowing intergenomic comparison of data thanks to dedicated search engines. The CAIAP server is hosted at www4.unifi.it/scibio/bioinfo/caiap/html. The programs (maintained as Perl scripts) are also available for download at the same location.
PGV – Prokaryotic Genome Viewer
Authors: Marla Soma , Singh Vinay Kumar
Published: January 2025

Recent sequencing of genomes of several microorganisms provides an opportunity to have access to huge volumes of data stored in various databases. This has resulted in the development of various computational and visualization tools to aid in retrieval and analysis of data. Development of user friendly genome data mapping and visualization tools facilitates researchers to closely examine various features of genes and make inferences from the displayed data efficiently. PGV – Prokaryotic Genome Viewer is a Java based web application tool capable of generating high quality interactive circular chromosome maps. With simple mouse roll over tasks on the interested region on the displayed map, the user is provided with features such as feature labeling, multi-fold zooming, image rotation and hyperlinking to different information resources. The tool is capable of instantaneously generating maps using user-supplied sequence data.
Cell Interaction Knowledgebase: An Online Database for Innate	Immune Cells, Cytokines and Chemokines
Authors: Chen Shu , Chinnaswamy Arunkumar , Biswas Subhra K. , Goryachev Andrew B. , Yap Choon-Kong , Lam Koi-Yau , Tay Cheng-Pei , Low Hong-Sang , Pokhilko Alexandra , Da-Jun Toh , Mishra Santosh K.
Published: January 2025

The innate immune system is fundamental to the recognition of pathogens, triggering of immune-inflammatory response and host defense. Recent advance in this area has resulted in enormous amount of data, which are stored across different databases. Integrating relevant information from these different data sources is difficult because of their heterogeneous nature and dispersed physical location.

We present here a single portal system, Cell Interaction Knowledgebase, with focus on the innate immunity. In particular, the knowledgebase houses comprehensive information on innate immune cells and cytokines/chemokines which are the principal mediators of communication among the immune cells. Currently the knowledgebase consists of extensive information on 2 major innate immune cell types (Macrophages and Dendritic cells) and 7 6 cytokines/chemokines for both human and mouse. In addition, intra-cellular molecular interactions and inter-cellular interactions involved in the innate immunity are curated and presented in an interactive and dynamic manner by animated pathways and query-driven cell-interaction map respectively.

This is one of the first databases that houses extensive phenotypic, signaling, genomic, proteomic and knockout data on both the innate immune cells and their attendant cytokines/chemokines, and is aimed to evolve as a one-stop-shop for immunologists. The first version of database is available at http://cell-interaction.bii.a-star.edu.sg/.
The Use of Multiple Hierarchically Independent Gene Ontology Terms	in Gene Function Prediction and Genome Annotation
Authors: Kourmpetis Yiannis A.I. , van der Burgt Ate , Bink Marco C.A.M. , ter Braak Cajo J.F. , van Ham Roeland C.H.J.
Published: January 2025

The Gene Ontology (GO) is a widely used controlled vocabulary for the description of gene function. In this study we quantify the usage of multiple and hierarchically independent GO terms in the curated genome annotations of seven well-studied species. In most genomes, significant proportions (6–60%) of genes have been annotated with multiple and hierarchically independent terms. This may be necessary to attain adequate specificity of description. One noticeable exception is Arabidopsis thaliana, in which genes are much less frequently annotated with multiple terms (6–14%). In contrast, an analysis of the occurrence of InterPro hits in the proteomes of the seven species, followed by a mapping of the hits to GO terms, did not reveal an aberrant pattern for the A. thaliana genome. This study shows the widespread usage of multiple hierarchically independent GO terms in the functional annotation of genes. By consequence, probabilistic methods that aim to predict gene function automatically through integration of diverse genomic datasets, and that employ the GO, must be able to predict such multiple terms.

We attribute the low frequency with which multiple GO terms are used in Arabidopsis to deviating practices in the genome annotation and curation process between communities of annotators. This may bias genome-scale comparisons of gene function between different species. GO term assignment should therefore be performed according to strictly similar rules and standards.
Structural Considerations for Designing Adenosine Analogs as	Selective Inhibitors of Trichomonas sp. Glyceraldehyde-3-	Phosphate Dehydrogenase
Authors: Kundu Sangeeta , Roy Debjani
Published: January 2025

Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) of pathogenic protozoa Trichomonas vaginalis (TvGAPDH) is an attractive drug target since this parasite lacks functional citric acid cycle and is dependent solely on glycolysis for its energy requirements. The three dimensional structure of TvGAPDH dimer has been generated by homology modelling based on the crystal structure of human liver GAPDH. Comparison of the NAD $^{+}$ binding pocket of the modeled TvGAPDH with human GAPDH (hGAPDH) reveals the presence of a hydrophobic pocket near the N-6 position of adenine ring as well as a hydrophobic cleft near O-2' of the adenosine ribose that are absent in the human enzyme. In order to exploit these structural differences adenosine and several adenosine analogs with substitution on N-6 position of adenine ring or 2' position of ribose sugar or both have been studied by docking experiments using the program AutoDock version 3.0.5. Our docking result suggests that bulkier hydrophobic substitution at the N-6 position of the adenine ring could form more stable complexes with TvGAPDH than with hGAPDH. An improvement of binding occurs in TvGAPDH when methoxybenzamido group has been introduced at the O-2' position of the ribose sugar. The combination of N-6 and O-2' substitutions may have produced significantly improved inhibitors. Our study may help in identifying structural elements involved in the origin of selectivity at the NAD $^{+}$ binding pocket of TvGAPDH. This study could further be extended for future anti-trichomonal drug design strategies in order to control trichomoniasis.
Identification of Novel Genomic Islands Associated with Small	RNAs
Authors: Sridhar Jayavel , Rafi Ziauddin Ahamed
Published: January 2025

Genome evolution in prokaryotes is assisted by integration of gene pools from phages and plasmids. Regions downstream of tRNAs and tmRNAs are considered as hot spots for the integration of these gene pools or genomic islands. Till date, genomic islands have been identified only at tRNA/tmRNA genes in the enterobacterial genomes. Present work reports 10 distinct small RNAs as potent integration sites for genomic islands. A known tool tRNAcc 1.0 has been used to identify genomic islands associated with small RNAs c0362, oxyS, ryaA, rybB, rybD, ryeB, ryeE, rtT, sraE and tmRNA. The coordinates of 25 such small RNA associated genomic islands in three E. coli (strains: CFT073, EDL933 and K12) and Shigella flexneri (strain: 301) genomes are presented. Moreover cross-verification of the genomic sequences encoded within the identified genomic islands in horizontal gene transfer database, GenBank annotation features and atypical sequence compositions support our results. Again, all of the identified 25 genomic integration sites do exhibit genomic block rearrangements with respect to the associated small RNA. Similar to tRNAs/tmRNAs, the downstream regions of the small RNAs are found to be hotspots of integration.
CGHsweep: An Algorithm for Analyzing Chromosomal Aberrations in	Genome Using aCGH Profiles
Authors: Karmaker Amitava , Kwek Stephen
Published: January 2025

DNA copy number aberrations along the genome are vital markers for studying pathogenesis of various diseases including cancers. Array-based Comparative Genome Hybridization (aCGH), which is a high-throughput cytogenetic method, helps in identifying genome-wide copy number aberrations, both gains and losses. Here, we propose a computational technique to analyze aCGH data and to identify potential DNA copy number alterations along the genome. Our technique detects the possible breakpoints by comparing contiguous probe log ratios, reports the aberrant segments and handles outliers to minimize false discovery rate. Empirically, we tested our algorithm on both prokaryotic (Brucella ovis) and eukaryotic (glioblastoma and colorectal cancer datasets from human) genomes. Our findings complement previous studies; our performance is competitive, sometimes superior, against other popular methods.
Fictive stress model calculations of viscoelastic behaviors in a Zr‐based glassy alloy
Authors: Kato H. , Inoue A. , Chen H.S.
Published: January 2025

Viscoelastic behaviors of a Zr‐based glassy alloy in the glass transition region were investigated with uni‐axial compression tests. The transition between the linear and the nonlinear viscoelasticity was observed just as it was observed in other glassy materials. The transition is considered to be due to a structural change in the glassy structure. Using the relationship taken from experimental results between the steady‐state flow stress and their relaxation time at various temperatures, we make the steady‐state flow stress, which is named fictive stress, stand for the flow structure during deformation. Based on the hypothesis of the stress‐induced structure change and the concept of the fictive stress, we succeeded in demonstrating various characteristic viscoelastic behaviors. The model calculations agreed fairly well with the experimental results. The condition under which the transition occurs and the mechanism of the transition itself in various deformations of a Zr‐based glassy alloy were demonstrated by this model calculation.
Specimen boundary induced size effect on quasi‐brittle fracture
Authors: Duan Kai , Hu Xiaozhi
Published: January 2025

A simple asymptotic analysis dealing with the influence of specimen boundaries on quasi‐brittle fracture of brittle heterogenous materials is presented. The influence of crack‐length/specimen‐size ratio (α‐ratio) on fracture behaviour of finite‐sized specimens has been quantitatively determined by the asymptotic boundary effect model. Different to the common size effect study relying on two scaling parameters that can only be determined by curve‐fitting to experimental results, the present boundary effect model is able to predict the quasi‐brittle fracture behaviour using two fundamental material properties: the tensile strength ft and fracture toughness KIC. ft and KIC can be determined separately following the standard strength and fracture toughness tests, or estimated from the available data in the literature for most commonly‐used materials. Alternatively, ft and KIC can be determined indirectly from quasi‐brittle fracture results using the boundary effect model so that the stringent tensile and fracture toughness tests can be replaced by the simple quasi‐brittle fracture tests. The boundary effect model shows the two scaling parameters used in the size effect study are α‐ratio dependent, and can only be taken as experimental constants for geometrically similar specimens of one single α‐ratio. Furthermore, un‐notched specimens containing small natural flaws have also been considered by the boundary effect model through linking the micro‐flaws to equivalent shallow notches with a near zero α‐ratio. As a result, both notched and un‐notched specimens can be analysed by the same boundary effect relationship.