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Developing computational tools that can facilitate the rational design of cell factories producing desired products at increased yields is challenging, as the tool needs to take into account that the preferred host organism usually has compounds that are consumed by competing reactions that reduce the yield of the desired product. On the other hand, the preferred host organisms may not have the native metabolic reactions needed to produce the compound of interest; thus, the computational tool needs to identify the metabolic reactions that will most efficiently produce the desired product. In this regard, we developed the generic tool PATHcre8 to facilitate an optimized search for heterologous biosynthetic pathway routes. PATHcre8 finds and ranks biosynthesis routes in a large number of organisms, including Cyanobacteria. The tool ranks the pathways based on feature scores that reflect reaction thermodynamics, the potentially toxic products in the pathway (compound toxicity), intermediate products in the pathway consumed by competing reactions (product consumption), and host-specific information such as enzyme copy number. A comparison with several other similar tools shows that PATHcre8 is more efficient in ranking functional pathways. To illustrate the effectiveness of PATHcre8, we further provide case studies focused on isoprene production and the biodegradation of cocaine. PATHcre8 is free for academic and nonprofit users and can be accessed at https://www.cbrc.kaust.edu.sa/pathcre8/.

Coronavirus infectious disease 2019 (COVID-19), a viral infection caused by a novel coronavirus (nCoV), continues to emerge as a serious threat to public health. This pandemic caused by SARS-CoV-2 (severe acute respiratory syndrome–coronavirus-2) has infected globally with 1,550,000 plus deaths to date, representing a high risk to public health. No effective drug or vaccine is available to curb down this deadly virus. The expedition for searching for a potential drug or vaccine against COVID-19 is of massive potential and favour to the community. This study is focused on finding an effective natural compound that can be processed further into a potential inhibitor to check the activity of SARS-CoV-2 with minimal side effects targeting NSP15 protein, which belongs to the EndoU enzyme family. The natural screening suggested two efficient compounds (PubChem ID: 95372568 and 1776037) with dihydroxyphenyl region of the compound, found to be important in the interaction with the viral protein showing promising activity which may act as a potent lead inhibitory molecule against the virus. In combination with virtual screening, modelling, drug likeliness, molecular docking, and 500 ns cumulative molecular dynamics simulations (100 ns for each complex) along with the decomposition analysis to calculate and confirm the stability and fold, we propose 95372568 and 1776037 as novel compounds of natural origin capable of getting developed into potent lead molecules against SARS-CoV-2 target protein NSP15. Supplementary Information The online version contains supplementary material available at 10.1007/s00894-021-04750-z.

The ongoing pandemic COVID-19 (COrona Virus Immuno Deficiency-2019) which is caused by SARS-CoV-2 (Severe Acute Respiratory Syndrome–CoronaVirus-2) has emerged as a pandemic with 400,000 plus deaths till date. We do not have any drug or vaccine available for the inhibition of this deadly virus. The expedition for searching a potential drug or vaccine against COVID-19 will be of massive potential and favor. This study is focused on finding an effective natural origin compound which can put a check on the activity of this virus. We chose important proteins from the SARS-CoV-2 genome such as NSP4, NSP15 and RdRp along-with the human ACE2 receptor which is the first point of contact with the virus. Virtual screening and followed up molecular docking resulted in Baicalin and Limonin as the final lead molecules. 200 ns of MD simulation for each protein-ligand complex provides the insights that Baicalin has a potential to target NSP4, NSP15 and RdRp proteins. Limonin which is largely used in traditional Indian medicine system is found to inhibit the human ACE2 receptor (making it inefficient in binding to the receptor binding domain of SARS-CoV-2). Our studies propose Baicalin and Limonin in combination to be studied in vitro and in vivo against COVID-19. Communicated by Ramaswamy H. Sarma

Pseudomonas genus is among the top nosocomial pathogens known to date. Being highly opportunistic, members of pseudomonas genus are most commonly connected with nosocomial infections of urinary tract and ventilator-associated pneumonia. Nevertheless, vaccine development for this pathogenic genus is slow because of no information regarding immunity correlated functional mechanism. In this present work, an immunoinformatics pipeline is used for vaccine development based on epitope-based peptide design, which can result in crucial immune response against outer membrane proteins of pseudomonas genus. A total of 127 outer membrane proteins were analysed, studied and out of them three sequences were obtained to be the producer of non-allergic, highly antigenic T-cell and B-cell epitopes which show good binding affinity towards class II HLA molecules. After performing rigorous screening utilizing docking, simulation, modelling techniques, we had one nonameric peptide (WLLATGIFL) as a good vaccine candidate. The predicted epitopes needs to be further validated for its apt use as vaccine. This work paves a new way with extensive therapeutic application against Pseudomonas genus and their associated diseases.

Microorganisms produce an enormous variety of chemical compounds. It is of general interest for microbiology and biotechnology researchers to have means to explore information about molecular and genetic basis of functioning of different microorganisms and their ability for bioproduction. To enable such exploration, we compiled 45 topic-specific knowledgebases (KBs) accessible through DESM portal (www.cbrc.kaust.edu.sa/desm). The KBs contain information derived through text-mining of PubMed information and complemented by information data-mined from various other resources (e.g. ChEBI, Entrez Gene, GO, KOBAS, KEGG, UniPathways, BioGrid). All PubMed records were indexed using 4,538,278 concepts from 29 dictionaries, with 1 638 986 records utilized in KBs. Concepts used are normalized whenever possible. Most of the KBs focus on a particular type of microbial activity, such as production of biocatalysts or nutraceuticals. Others are focused on specific categories of microorganisms, e.g. streptomyces or cyanobacteria. KBs are all structured in a uniform manner and have a standardized user interface. Information exploration is enabled through various searches. Users can explore statistically most significant concepts or pairs of concepts, generate hypotheses, create interactive networks of associated concepts and export results. We believe DESM will be a useful complement to the existing resources to benefit microbiology and biotechnology research.

Background Cyanobacteria are one of the target groups of organisms explored for production of free fatty acids (FFAs) as biofuel precursors. Experimental evaluation of cyanobacterial potential for FFA production is costly and time consuming. Thus, computational approaches for comparing and ranking cyanobacterial strains for their potential to produce biofuel based on the characteristics of their predicted proteomes can be of great importance. Results To enable such comparison and ranking, and to assist biotechnology developers and researchers in selecting strains more likely to be successfully engineered for the FFA production, we developed the Biofuel Producer Screen (BioPS) platform (http://www.cbrc. kaust.edu.sa/biops). BioPS relies on the estimation of the predicted proteome makeup of cyanobacterial strains to produce and secrete FFAs, based on the analysis of well-studied cyanobacterial strains with known FFA production profiles. The system links results back to various external repositories such as KEGG, UniProt and GOLD, making it easier for users to explore additional related information. Conclusion To our knowledge, BioPS is the first tool that screens and evaluates cyanobacterial strains for their potential to produce and secrete FFAs based on strain's predicted proteome characteristics, and rank strains based on that assessment. We believe that the availability of such a platform (comprising both a prediction tool and a repository of pre-evaluated stains) would be of interest to biofuel researchers. The BioPS system will be updated annually with information obtained from newly sequenced cyanobacterial genomes as they become available, as well as with new genes that impact FFA production or secretion.

Identifying transcription factor (TF) binding sites (TFBSs) is important in the computational inference of gene regulation. Widely used computational methods of TFBS prediction based on position weight matrices (PWMs) usually have high false positive rates. Moreover, computational studies of transcription regulation in eukaryotes frequently require numerous PWM models of TFBSs due to a large number of TFs involved. To overcome these problems we developed DRAF, a novel method for TFBS prediction that requires only 14 prediction models for 232 human TFs, while at the same time significantly improves prediction accuracy. DRAF models use more features than PWM models, as they combine information from TFBS sequences and physicochemical properties of TF DNA-binding domains into machine learning models. Evaluation of DRAF on 98 human ChIP-seq datasets shows on average 1.54-, 1.96- and 5.19-fold reduction of false positives at the same sensitivities compared to models from HOCOMOCO, TRANSFAC and DeepBind, respectively. This observation suggests that one can efficiently replace the PWM models for TFBS prediction by a small number of DRAF models that significantly improve prediction accuracy. The DRAF method is implemented in a web tool and in a stand-alone software freely available at http://cbrc.kaust.edu.sa/DRAF.

Mangroves are unique, and endangered, coastal ecosystems that play a vital role in the tropical and subtropical environments. A comprehensive description of the microbial communities in these ecosystems is currently lacking, and additional studies are required to have a complete understanding of the functioning and resilience of mangroves worldwide.In this work, we carried out a metagenomic study by comparing the microbial community of mangrove sediment with the rhizosphere microbiome of Avicennia marina, in northern Red Sea mangroves, along the coast of Saudi Arabia. Our results revealed that rhizosphere samples presented similar profiles at the taxonomic and functional levels and differentiated from the microbiome of bulk soil controls. Overall, samples showed predominance by Proteobacteria, Bacteroidetes and Firmicutes, with high abundance of sulfate reducers and methanogens, although specific groups were selectively enriched in the rhizosphere. Functional analysis showed significant enrichment in ‘metabolism of aromatic compounds’, ‘mobile genetic elements’, ‘potassium metabolism’ and ‘pathways that utilize osmolytes’ in the rhizosphere microbiomes.To our knowledge, this is the first metagenomic study on the microbiome of mangroves in the Red Sea, and the first application of unbiased 454-pyrosequencing to study the rhizosphere microbiome associated with A. marina. Our results provide the first insights into the range of functions and microbial diversity in the rhizosphere and soil sediments of gray mangrove (A. marina) in the Red Sea.

Background Finding a source from which high-energy-density biofuels can be derived at an industrial scale has become an urgent challenge for renewable energy production. Some microorganisms can produce free fatty acids (FFA) as precursors towards such high-energy-density biofuels. In particular, photosynthetic cyanobacteria are capable of directly converting carbon dioxide into FFA. However, current engineered strains need several rounds of engineering to reach the level of production of FFA to be commercially viable; thus new chassis strains that require less engineering are needed. Although more than 120 cyanobacterial genomes are sequenced, the natural potential of these strains for FFA production and excretion has not been systematically estimated. Results Here we present the FFA SC (FFASC), an in silico screening method that evaluates the potential for FFA production and excretion of cyanobacterial strains based on their proteomes. A literature search allowed for the compilation of 64 proteins, most of which influence FFA production and a few of which affect FFA excretion. The proteins are classified into 49 orthologous groups (OGs) that helped create rules used in the scoring/ranking of algorithms developed to estimate the potential for FFA production and excretion of an organism. Among 125 cyanobacterial strains, FFASC identified 20 candidate chassis strains that rank in their FFA producing and excreting potential above the specifically engineered reference strain, Synechococcus sp. PCC 7002. We further show that the top ranked cyanobacterial strains are unicellular and primarily include Prochlorococcus (order Prochlorales) and marine Synechococcus (order Chroococcales) that cluster phylogenetically. Moreover, two principal categories of enzymes were shown to influence FFA production the most: those ensuring precursor availability for the biosynthesis of lipids, and those involved in handling the oxidative stress associated to FFA synthesis. Conclusion To our knowledge FFASC is the first in silico method to screen cyanobacteria proteomes for their potential to produce and excrete FFA, as well as the first attempt to parameterize the criteria derived from genetic characteristics that are favorable/non-favorable for this purpose. Thus, FFASC helps focus experimental evaluation only on the most promising cyanobacteria. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-3389-4) contains supplementary material, which is available to authorized users.