Antimicrobial peptide identification using multi-scale convolutional network

Background: Antibiotic resistance has become an increasingly serious problem in the past decades. As an alternative choice, antimicrobial peptides (AMPs) have attracted lots of attention. To identify new AMPs, machine learning methods have been commonly used. More recently, some deep learning methods have also been applied to this problem. Results: In this paper, we designed a deep learning model to identify AMP sequences. We employed the embedding layer and the multi-scale convolutional network in our model. The multi-scale convolutional network, which contains multiple convolutional layers of varying filter lengths, could utilize all latent features captured by the multiple convolutional layers. To further improve the performance, we also incorporated additional information into the designed model and proposed a fusion model. Results showed that our model outperforms the state-of-the-art models on two AMP datasets and the Antimicrobial Peptide Database (APD)3 benchmark dataset. The fusion model also outperforms the state-of-the-art model on an anti-inflammatory peptides (AIPs) dataset at the accuracy. Conclusions: Multi-scale convolutional network is a novel addition to existing deep neural network (DNN) models. The proposed DNN model and the modified fusion model outperform the state-of-the-art models for new AMP discovery. The source code and data are available at https://github.com/zhanglabNKU/APIN

Bioengineering Lantibiotics for Therapeutic Success

peer-reviewedSeveral examples of highly modified antimicrobial peptides have been described. While many such peptides are non-ribosomally synthesized, ribosomally synthesized equivalents are being discovered with increased frequency. Of the latter group, the lantibiotics continue to attract most attention. In the present review, we discuss the implementation of in vivo and in vitro engineering systems to alter, and even enhance, the antimicrobial activity, antibacterial spectrum and physico-chemical properties, including heat stability, solubility, diffusion and protease resistance, of these compounds. Additionally, we discuss the potential applications of these lantibiotics for use as therapeutics.DF,CH,PC,RR are supported by the Irish Government under the National Development Plan, through a Science Foundation Ireland (SFI) Technology and Innovation Development Award (TIDA14/TIDA/2286) to DF, a SFI Investigator awards to CH and RR (10/IN.1/B3027),SFI-PIfunding(11/PI/1137) to PDC and the Alimentary Pharmabiotic Centre under Grant Number SFI/12/RC/2273

Causal Graphs Underlying Generative Models: Path to Learning with Limited Data

Training generative models that capture rich semantics of the data and interpreting the latent representations encoded by such models are very important problems in unsupervised learning. In this work, we provide a simple algorithm that relies on perturbation experiments on latent codes of a pre-trained generative autoencoder to uncover a causal graph that is implied by the generative model. We leverage pre-trained attribute classifiers and perform perturbation experiments to check for influence of a given latent variable on a subset of attributes. Given this, we show that one can fit an effective causal graph that models a structural equation model between latent codes taken as exogenous variables and attributes taken as observed variables. One interesting aspect is that a single latent variable controls multiple overlapping subsets of attributes unlike conventional approach that tries to impose full independence. Using a pre-trained RNN-based generative autoencoder trained on a dataset of peptide sequences, we demonstrate that the learnt causal graph from our algorithm between various attributes and latent codes can be used to predict a specific property for sequences which are unseen. We compare prediction models trained on either all available attributes or only the ones in the Markov blanket and empirically show that in both the unsupervised and supervised regimes, typically, using the predictor that relies on Markov blanket attributes generalizes better for out-of-distribution sequences

Biodegradable polymeric micro/nano-structures with intrinsic antifouling/antimicrobial properties: relevance in damaged skin and other biomedical applications

Bacterial colonization of implanted biomedical devices is the main cause of healthcare-associated infections, estimated to be 8.8 million per year in Europe. Many infections originate from damaged skin, which lets microorganisms exploit injuries and surgical accesses as passageways to reach the implant site and inner organs. Therefore, an effective treatment of skin damage is highly desirable for the success of many biomaterial-related surgical procedures. Due to gained resistance to antibiotics, new antibacterial treatments are becoming vital to control nosocomial infections arising as surgical and post-surgical complications. Surface coatings can avoid biofouling and bacterial colonization thanks to biomaterial inherent properties (e.g., super hydrophobicity), specifically without using drugs, which may cause bacterial resistance. The focus of this review is to highlight the emerging role of degradable polymeric micro- and nano-structures that show intrinsic antifouling and antimicrobial properties, with a special outlook towards biomedical applications dealing with skin and skin damage. The intrinsic properties owned by the biomaterials encompass three main categories: (1) physical–mechanical, (2) chemical, and (3) electrostatic. Clinical relevance in ear prostheses and breast implants is reported. Collecting and discussing the updated outcomes in this field would help the development of better performing biomaterial-based antimicrobial strategies, which are useful to prevent infections

Biodegradable polymeric micro/Nano-structures with intrinsic antifouling/antimicrobial properties: Relevance in damaged skin and other biomedical applications

Bacterial colonization ofimplanted biomedical devicesis themain cause of healthcare-associated infections, estimated to be 8.8 million per year in Europe. Many infections originate from damaged skin, which lets microorganisms exploit injuries and surgical accesses as passageways to reach the implant site and inner organs. Therefore, an effective treatment of skin damage is highly desirable for the success of many biomaterial-related surgical procedures. Due to gained resistance to antibiotics, new antibacterial treatments are becoming vital to control nosocomial infections arising as surgical and post-surgical complications. Surface coatings can avoid biofouling and bacterial colonization thanks to biomaterial inherent properties (e.g., super hydrophobicity), specifically without using drugs, which may cause bacterial resistance. The focus of this review is to highlight the emerging role of degradable polymeric micro- and nano-structures that show intrinsic antifouling and antimicrobial properties, with a special outlook towards biomedical applications dealing with skin and skin damage. The intrinsic properties owned by the biomaterials encompass three main categories: (1) physical-mechanical, (2) chemical, and (3) electrostatic. Clinical relevance in ear prostheses and breast implants is reported. Collecting and discussing the updated outcomes in this field would help the development of better performing biomaterial-based antimicrobial strategies, which are useful to prevent infections

Review on bibliography related to antimicrobials

In this report, a bibliographic research has been done in the field of antimicrobials.In this report, a bibliographic research has been done in the field of antimicrobials. Not all antimicrobials have been included, but those that are being subject of matter in the group GBMI in Terrassa, and others of interest. It includes chitosan and other biopolymers. The effect of nanoparticles is of great interest, and in this sense, the effect of Ag nanoparticles and antibiotic nanoparticles (nanobiotics) has been revised. The report focuses on new publications and the antimicrobial effect of peptides has been considered. In particular, the influence of antimicrobials on membranes has deserved much attention and its study using the Langmuir technique, which is of great utility on biomimetic studies. The building up of antimicrobials systems with new techniques (bottom-up approach), as the Layer-by-Layer technique, can also be found in between the bibliography. It has also been considered the antibiofilm effect, and the new ideas on quorem sensing and quorum quenching.Preprin

Estudios computacionales de mecanismos moleculares de la inmunidad innata

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Farmacia, leída el 20-12-2022Antimicrobial Resistance (AMR) is a worldwide health emergency. ESKAPE pathogens include the most relevant AMR bacterial families. In particular, Gram-negative bacteria stand out due to their cell envelope complexity, which exhibits strong resistance to antimicrobials. A key element for AMR is the chemical structure of bacterial lipopolysaccharide (LPS), and the phospholipid composition of the membrane, inflecting the membrane permeability to antibiotics. We have applied coarse-grained molecular dynamics simulations to capture the role of the phospholipid composition and lipid A structure in the membrane properties and morphology of ESKAPE Gram-negative bacterial vesicles. Moreover, the reported antimicrobial peptides Cecropin B1, JB95, and PTCDA1-kf were used to unveil their implications for membrane disruption. This study opens a promising starting point for understanding the molecular keys of bacterial membranes and promoting the discovery of new antimicrobials to overcome AMR...La resistencia a los antimicrobianos (AMR) es una emergencia sanitaria mundial. Los patógenos ESKAPE incluyen las familias bacterianas más resistentes a antibióticos y son altamente virulentas. En particular, las bacterias Gram negativas destacan por la complejidad de su pared celular, que presenta una fuerte resistencia frente a los antibióticos. Un elemento clave para la AMR es la estructura química del lipopolisacárido bacteriano (LPS) y la composición de los fosfolípidos de la membrana bacteriana, que influyen en su permeabilidad a los antibióticos. Se han empleado simulaciones de dinámica molecular de grano grueso para captar el papel de la composición de los fosfolípidos y la estructura del LPS en las propiedades y morfología de modelos de vesículas bacterianas Gram negativas ESKAPE. Además, se han empleado los péptidos antimicrobianos Cecropin B1, JB95 y PTCDA1-kf para desvelar su mecanismo disrupción de la membrana bacteriana. Este estudio abre un prometedor punto de partida para comprender las claves moleculares de la resistencia en membranas bacterianas y acelerar el descubrimiento de nuevos antibióticos para hacer frente a la AMR...Fac. de FarmaciaTRUEunpu

Hydrophobic and hydrophilic au and ag nanoparticles. Breakthroughs and perspectives

This review provides a broad look on the recent investigations on the synthesis, characterization and physico-chemical properties of noble metal nanoparticles, mainly gold and silver nanoparticles, stabilized with ligands of different chemical nature. A comprehensive review of the available literature in this field may be far too large and only some selected representative examples will be reported here, together with some recent achievements from our group, that will be discussed in more detail. Many efforts in finding synthetic routes have been performed so far to achieve metal nanoparticles with well-defined size, morphology and stability in different environments, to match the large variety of applications that can be foreseen for these materials. In particular, the synthesis and stabilization of gold and silver nanoparticles together with their properties in different emerging fields of nanomedicine, optics and sensors are reviewed and briefly commented