With the explosive growth of biological sequences generated in the post-genomic era, one of the most challenging problems in bioinformatics and computational biology is to computationally characterize sequences, structures and functions in an efficient, accurate and high-throughput manner. A number of online web servers and stand-alone tools have been developed to address this to date; however, all these tools have their limitations and drawbacks in terms of their effectiveness, user-friendliness and capacity. Here, we present iLearn, a comprehensive and versatile Python-based toolkit, integrating the functionality of feature extraction, clustering, normalization, selection, dimensionality reduction, predictor construction, best descriptor/model selection, ensemble learning and results visualization for DNA, RNA and protein sequences. iLearn was designed for users that only want to upload their data set and select the functions they need calculated from it, while all necessary procedures and optimal settings are completed automatically by the software. iLearn includes a variety of descriptors for DNA, RNA and proteins, and four feature output formats are supported so as to facilitate direct output usage or communication with other computational tools. In total, iLearn encompasses 16 different types of feature clustering, selection, normalization and dimensionality reduction algorithms, and five commonly used machine-learning algorithms, thereby greatly facilitating feature analysis and predictor construction. iLearn is made freely available via an online web server and a stand-alone toolkit.

Antimicrobial peptides (AMPs) are distributed across all kingdoms of life and are an indispensable component of host defenses. They consist of predominantly short cationic peptides with a wide variety of structures and targets. Given the ever-emerging resistance of various pathogens to existing antimicrobial therapies, AMPs have recently attracted extensive interest as potential therapeutic agents. As the discovery of new AMPs has increased, many databases specializing in AMPs have been developed to collect both fundamental and pharmacological information. In this review, we summarize the sources, structures, modes of action, and classifications of AMPs. Additionally, we examine current AMP databases, compare valuable computational tools used to predict antimicrobial activity and mechanisms of action, and highlight new machine learning approaches that can be employed to improve AMP activity to combat global antimicrobial resistance.

The emergence of multidrug-resistant (MDR) Gram-negative pathogens is an urgent global medical challenge. The old polymyxin lipopeptide antibiotics (polymyxin B and colistin) are often the only therapeutic option due to resistance to all other classes of antibiotics and the lean antibiotic drug development pipeline. However, polymyxin B and colistin suffer from major issues in safety (dose-limiting nephrotoxicity, acute toxicity), pharmacokinetics (poor exposure in the lungs) and efficacy (negligible activity against pulmonary infections) that have severely limited their clinical utility. Here we employ chemical biology to systematically optimize multiple non-conserved positions in the polymyxin scaffold, and successfully disconnect the therapeutic efficacy from the toxicity to develop a new synthetic lipopeptide, structurally and pharmacologically distinct from polymyxin B and colistin. This resulted in the clinical candidate F365 (QPX9003) with superior safety and efficacy against lung infections caused by top-priority MDR pathogens Pseudomonas aeruginosa, Acinetobacter baumannii and Klebsiella pneumoniae.

Cyanobacteria are oxygenic photosynthetic prokaryotes that play important roles in the global carbon cycle. Recently, engineered cyanobacteria capable of producing various small molecules from CO2 have been developed. However, cyanobacteria are seldom considered as factories for producing proteins, mainly because of the lack of efficient strong promoters. Here, we report the discovery and verification of a super-strong promoter Pcpc560, which contains two predicted promoters and 14 predicted transcription factor binding sites (TFBSs). Using Pcpc560, functional proteins were produced at a level of up to 15% of total soluble protein in the cyanobacterium Synechocystis sp. 6803, a level comparable to that produced in Escherichia coli. We demonstrated that the presence of multiple TFBSs in Pcpc560 is crucial for its promoter strength. Genetically transformable cyanobacteria neither have endotoxins nor form inclusion bodies; therefore, Pcpc560 opens the possibility to use cyanobacteria as alternative hosts for producing heterogeneous proteins from CO2 and inorganic nutrients.

Solvent production by Clostridium acetobutylicum collapses when cells are grown in pH-uncontrolled glucose medium, the so-called “acid crash” phenomenon. It is generally accepted that the fast accumulation of acetic acid and butyric acid triggers the acid crash. We found that addition of 1 mM formic acid into corn mash medium could trigger acid crash, suggesting that formic acid might be related to acid crash. When it was grown in pH-uncontrolled glucose medium or glucose-rich medium, C. acetobutylicum DSM 1731 containing the empty plasmid pIMP1 failed to produce solvents and was found to accumulate 0.5 to 1.24 mM formic acid intracellularly. In contrast, recombinant strain DSM 1731 with formate dehydrogenase activity did not accumulate formic acid intracellularly and could produce solvent as usual. We therefore conclude that the accumulation of formic acid, rather than acetic acid and butyric acid, is responsible for the acid crash of acetone-butanol-ethanol fermentation.