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Abstract: Neurodegenerative diseases affect over 30 million people worldwide with an ascending trend. Most individuals suffering from these irreversible brain damages belong to the elderly population, with onset between 50 and 60 years. Although the pathophysiology of such diseases is partially known, it remains unclear upon which point a disease turns degenerative. Moreover, current therapeutics can treat some of the symptoms but often have severe side effects and become less effective in long-term treatment. For many neurodegenerative diseases, the involvement of G proteincoupled receptors (GPCRs), which are key players of neuronal transmission and plasticity, has become clearer and holds great promise in elucidating their biological mechanism. With this review, we introduce and summarize class A and class C GPCRs, known to form heterodimers or oligomers to increase their signalling repertoire. Additionally, the examples discussed here were shown to display relevant alterations in brain signalling and had already been associated with the pathophysiology of certain neurodegenerative diseases. Lastly, we classified the heterodimers into two categories of crosstalk, positive or negative, for which there is known evidence.

Electroactive smart materials play an important role for tissue regenerative applications. Poly(vinylidene fluoride) (PVDF) is a specific subtype of piezoelectric electroactive material that generates electrical potential upon mechanical stimulation. This work focuses on the application of piezoelectric PVDF films for neural differentiation. Human neural precursor cells (hNPCs) are cultured on piezoelectric poled and non-poled -PVDF films with or without a pre-coating step of poly-d-lysine and laminin (PDL/L). Subsequently, hNPCs differentiation into the neuronal lineage is assessed (MAP2+ and DCX+) under static or dynamic (piezoelectric stimulation) culture conditions. The results demonstrate that poled and coated -PVDF films induce neuronal differentiation under static culture conditions which is further enhanced with mechanical stimulation. In silico calculations of the electrostatic potential of different domains of laminin, highlight the high polarity of those domains, which shows a clear preference to interact with the varying surface electric field of the piezoelectric material under mechanical stimulation. These interactions might explain the higher neuronal differentiation induced by poled -PVDF films pre-coated with PDL/L under dynamic conditions. Our results suggest that electromechanical stimuli, such as the ones induced by piezoelectric -PVDF films, are suitable to promote neuronal differentiation and hold great promise for the development of neuroregenerative therapies., Fundação para a Ciência e a Tecnologia, Grant/Award Numbers: POCI-01-0145-FEDER-029206, POCI-01-0145-FEDER-031392, PTDC/MED-NEU/31417/2017, UIDB/50026/2020, UIDP/50026/2020, NORTE-01-0145-FEDER-029968, POCI-01-0145-FEDER-029751, POCI-01-0145-FEDER-032619, UID/FIS/04650/2020, PTDC/EMD-EMD/28159/2017, PTDC/BTM-MAT/28237/2017, PTDC/QUI-OUT/32243/2017, LA/P/0058/2020, UIDB/04539/2020, UIDP/04539/2020, POCI-01-0145-FEDER-031356, DSAIPA/DS/0118/2020; Santa Casa da Misericórdia de Lisboa, Grant/Award Numbers: MC-04-17, MC-18-21; Basque Government Industry Departments; Spanish State Research Agency; European Regional Development Fund, Grant/Award Number: PID2019-106099RB-C43/AEI; Northern Portugal Regional Operational Program, Grant/Award Numbers: NORTE-01-0145-FEDER-000023, NORTE-01-0145-FEDER-000013; Portuguese Platform of Bioimaging, Grant/Award Number: PPBI-POCI-01-0145-FEDER-022122; ICVS Scientific Microscopy Platform; Portuguese Foundation for Science and Technology, Grant/Award Number: PD/BDE/143150/2019, info:eu-repo/semantics/publishedVersion

A sequencing batch reactor (SBR) inoculated with activated sludge and bioaugmented with a dye-decolorizing yeast strain—Yarrowia lipolytica (HOMOGST27AB) was assembled to form yeast-bioaugmented aerobic granular sludge (AGS). The bioaugmented AGS-SBR was operated for the treatment of synthetic saline wastewater (12 g L−1) intermittently fed with a reactive textile dye (Navy Everzol ED) at 25, 15, and 7.5 mg L−1. Dye degradation did not occur, although some dye adsorbed to the granules. AGS-SBR performance in removing carbon and nitrogen was good and was not affected by the dye addition. Bioaugmentation with the yeast Y. lipolytica (HOMOGST27AB) occurred with success, proved by sequencing samples from granules throughout the reactor operation. The AGS core microbiome gathered essentially microorganisms from the Proteobacteria and Bacteroidetes phyla. The microbial profile showed a dynamic microbiome established at Phase I of the operation, with a high decrease in the abundance of Ignavibacterium from the initial biomass to the granules formed and an increase in Actinobacteria, Cytophagia, Flavobacteria, and Alphaproteobacteria in the remaining phases of the bioreactor operation.

This special edition intends to highlight how omics approaches have been used in biodegradation studies to understand the mechanisms involved and improve biodegradation processes [...]

Mutations linked to neurodevelopmental disorders, such as intellectual disability (ID), are frequently found in genes that encode for proteins of the excitatory synapse. Transmembrane AMPA receptor regulatory proteins (TARPs) are AMPA receptor auxiliary proteins that regulate crucial aspects of receptor function. Here, we investigate a mutant form of the TARP family member stargazin, described in an ID patient. Molecular dynamics analyses predicted that the ID-associated stargazin variant, V143L, weakens the overall interface of the AMPAR:stargazin complex and impairs the stability of the complex. Knock-in mice harboring the V143L stargazin mutation manifest cognitive and social deficits and hippocampal synaptic transmission defects, resembling phenotypes displayed by ID patients. In the hippocampus of stargazin V143L mice, CA1 neurons show impaired spine maturation, abnormal synaptic transmission and long-term potentiation specifically in basal dendrites, and synaptic ultrastructural alterations. These data suggest a causal role for mutated stargazin in the pathogenesis of ID and unveil a new role for stargazin in the development and function of hippocampal synapses. This work was supported by a NARSAD Independent Investigator Grant (#23151) and a NARSAD Young Investigator Grant (#20733) from the Brain and Behavior Research Foundation, by a research grant from the Jérôme Lejeune Foundation (#1530), by “la Caixa” Foundation (ID 100010434), and FCT, I.P under the project code LCF/PR/HP20/52300003, by a Marie Curie Integration Grant (618525), by a Bial Foundation Grant (266/2016), by national funds through the Portuguese Science and Technology Foundation (FCT: UID/NEU/04539/2013, UIDB/04539/2020, POCI-01-0145-FEDER-28541, POCI-01-0145-FEDER-016682, PTDC/QUI-OUT/32243/2017 and CPCA/A0/7302/2020), and by the European Regional Development Fund (ERDF), through the Centro 2020 Regional Operational Programme, under project CENTRO-01-0145-FEDER-000008:BrainHealth 2020. GLC, NB, MVR, ME and CAVB were supported by FCT through Ph.D. scholarships SFRH/BD/51962/2012, SFRH/BD/144881/2019, SFRH/BD/129236/2017, SFRH/BD/51958/2012 and SFRH/BD/145457/2019, respectively. ASI and JG were supported by FCT through Postdoctoral fellowship SFRH/BPD122299/2016 and SFRH/BPD/120611/2016, respectively. RPG and RM received support from FCT/DGES, under the program “Verão com Ciência”.

SARS-CoV-2 triggered a worldwide pandemic disease, COVID-19, for which an effective treatment has not yet been settled. Among the most promising targets to fight this disease is SARS-CoV-2 main protease (Mpro), which has been extensively studied in the last few months. There is an urgency for developing effective computational protocols that can help us tackle these key viral proteins. Hence, we have put together a robust and thorough pipeline of in silico protein–ligand characterization methods to address one of the biggest biological problems currently plaguing our world. These methodologies were used to characterize the interaction of SARS-CoV-2 Mpro with an α-ketoamide inhibitor and include details on how to upload, visualize, and manage the three-dimensional structure of the complex and acquire high-quality figures for scientific publications using PyMOL (Protocol 1); perform homology modeling with MODELLER (Protocol 2); perform protein–ligand docking calculations using HADDOCK (Protocol 3); run a virtual screening protocol of a small compound database of SARS-CoV-2 candidate inhibitors with AutoDock 4 and AutoDock Vina (Protocol 4); and, finally, sample the conformational space at the atomic level between SARS-CoV-2 Mpro and the α-ketoamide inhibitor with Molecular Dynamics simulations using GROMACS (Protocol 5). Guidelines for careful data analysis and interpretation are also provided for each Protocol.

Text mining (TM) is a semi-automatized, multi-step process, able to turn unstructured into structured data. TM relevance has increased upon machine learning (ML) and deep learning (DL) algorithms’ application in its various steps. When applied to biomedical literature, text mining is named biomedical text mining and its specificity lies in both the type of analyzed documents and the language and concepts retrieved. The array of documents that can be used ranges from scientific literature to patents or clinical data, and the biomedical concepts often include, despite not being limited to genes, proteins, drugs, and diseases. This review aims to gather the leading tools for biomedical TM, summarily describing and systematizing them. We also surveyed several resources to compile the most valuable ones for each category.

Thiamethoxam (TMX) is an effective neonicotinoid insecticide. However, its widespread use is detrimental to non-targeted organisms and water systems. This study investigates the biodegradation of this insecticide by

Diclofenac is a worldwide consumed drug included in the watch list of substances to be monitored according to the European Union Water Framework Directive (Directive 2013/39/EU). Aerobic granular sludge sequencing batch reactors (AGS-SBR) are increasingly used for wastewater treatment but there is scant information on the fate and effect of micropollutants to nutrient removal processes. An AGS-SBR fed with synthetic wastewater containing diclofenac was bioaugmented with a diclofenac degrading bacterial strain and performance and microbial community dynamics was analysed. Chemical oxygen demand, phosphate and ammonia removal were not affected by the micropollutant at 0.03 mM (9.54 mg L-1). The AGS was able to retain the degrading strain, which was detected in the sludge throughout after augmentation. Nevertheless, besides some adsorption to the biomass, diclofenac was not degraded by the augmented sludge given the short operating cycles and even if batch degradation assays confirmed that the bioaugmented AGS was able to biodegrade the compound. The exposure to the pharmaceutical affected the microbial community of the sludge, separating the two first phases of reactor operation (acclimatization and granulation) from subsequent phases. The AGS was able to keep the bioaugmented strain and to maintain the main functions of nutrient removal even through the long exposure to the pharmaceutical, but combined strategies are needed to reduce the spread of micropollutants in the environment.

Endocrine disrupting compounds (EDCs) in the environment are considered a motif of concern, due to the widespread occurrence and potential adverse ecological and human health effects. The natural estrogen, 17β-estradiol (E2), is frequently detected in receiving water bodies after not being efficiently removed in conventional wastewater treatment plants (WWTPs), promoting a negative impact for both the aquatic ecosystem and human health. In this study, the biodegradation of E2 by Rhodococcus sp. ED55, a bacterial strain isolated from sediments of a discharge point of WWTP in Coloane, Macau, was investigated. Rhodococcus sp. ED55 was able to completely degrade 5 mg/L of E2 in 4 h in a synthetic medium. A similar degradation pattern was observed when the bacterial strain was used in wastewater collected from a WWTP, where a significant improvement in the degradation of the compound occurred. The detection and identification of 17 metabolites was achieved by means of UPLC/ESI/HRMS, which proposed a degradation pathway of E2. The acute test with luminescent marine bacterium Aliivibrio fischeri revealed the elimination of the toxicity of the treated effluent and the standardized yeast estrogenic (S-YES) assay with the recombinant strain of Saccharomyces cerevisiae revealed a decrease in the estrogenic activity of wastewater samples after biodegradation.

Viruses are a diverse biological group capable of infecting several hosts such as bacteria, plants, and animals, including humans. Viral infections constitute a threat to the human population as they may cause high mortality rates, decrease food production, and generate large economical losses. Viruses co-evolve with their hosts and this constant evolution must be clarified to better predict possible viral outbreaks, and to develop improved diagnostic methods and therapeutical approaches. In this review, we summarize several viral databases that store key information retrieved from a variety of omics approaches. Furthermore, we explore the use of such databases to predict Virus-Host interactions through artificial intelligence algorithms, focusing on the latest methodologies to characterize biological networks.

The pollution of water resources by pesticides poses serious problems for public health and the environment. In this study, Actinobacteria strains were isolated from three wastewater treatment plants (WWTPs) and were screened for their ability to degrade 17 pesticide compounds. Preliminary screening of 13 of the isolates of Actinobacteria allowed the selection of 12 strains with potential for the degradation of nine different pesticides as sole carbon source, including aliette, for which there are no previous reports of biodegradation. Evaluation of the bacterial growth and degradation kinetics of the pesticides 2,4-dichlorophenol (2,4-DCP) and thiamethoxam (tiam) by selected Actinobacteria strains was performed in liquid media. Strains Streptomyces sp. ML and Streptomyces sp. OV were able to degrade 45% of 2,4-DCP (50 mg/l) as the sole carbon source in 30 days and 84% of thiamethoxam (35 mg/l) in the presence of 10 mM of glucose in 18 days. The biodegradation of thiamethoxam by Actinobacteria strains was reported for the first time in this study. These strains are promising for use in bioremediation of ecosystems polluted by this type of pesticides.

Paediatric Acquired ImmunoDeficiency Syndrome (AIDS) is a life-threatening and infectious disease in which the Human Immunodeficiency Virus (HIV) is mainly transmitted through Mother-To- Child Transmission (MTCT) during pregnancy, labour and delivery, or breastfeeding. This review provides an overview of the distinct therapeutic alternatives to abolish the systemic viral replication in paediatric HIV-1 infection. Numerous classes of antiretroviral agents have emerged as therapeutic tools for downregulation of different steps in the HIV replication process. These classes encompass Non- Nucleoside Analogue Reverse Transcriptase Inhibitors (NNRTIs), Nucleoside/Nucleotide Analogue Reverse Transcriptase Inhibitors (NRTIs/NtRTIs), INtegrase Inhibitors (INIs), Protease Inhibitors (PIs), and Entry Inhibitors (EIs). Co-administration of certain antiretroviral drugs with Pharmacokinetic Enhancers (PEs) may boost the effectiveness of the primary therapeutic agent. The combination of multiple antiretroviral drug regimens (Highly Active AntiRetroviral Therapy - HAART) is currently the standard therapeutic approach for HIV infection. So far, the use of HAART offers the best opportunity for prolonged and maximal viral suppression, and preservation of the immune system upon HIV infection. Still, the frequent administration of high doses of multiple drugs, their inefficient ability to reach the viral reservoirs in adequate doses, the development of drug resistance, and the lack of patient compliance compromise the complete HIV elimination. The development of nanotechnology-based drug delivery systems may enable targeted delivery of antiretroviral agents to inaccessible viral reservoir sites at therapeutic concentrations. In addition, the application of Computer-Aided Drug Design (CADD) approaches has provided valuable tools for the development of anti-HIV drug candidates with favourable pharmacodynamics and pharmacokinetic properties.

Globally with over 10 million deaths per year, cancer is the most transversal disease across countries, cultures, and ethnicities, affecting both developed and developing regions. Tumorigenesis is dynamically altered by distinct events and can be lethal when untreated. Despite the innovative therapeutics available, multidrug resistance (MDR) to chemotherapy remains the major hindrance to the success of cancer therapy. The multiple mechanisms by which cancer cells evade cell death are diverse, indicating that MDR involves complex interconnected biological networks. Molecular profiling is currently able to stratify cancer into its distinct subtypes and help identify the best therapeutics, leading to "translational systems medicine". Highly specialized methodologies are generating a large amount of "omics" data - including epigenetics, genomics, transcriptomics, proteomics, metabolomics, as well as pharmacogenomics. Many of the resulting databases store data in non-standard formats, which need to be converted, interpreted, and merged into readable formats. The latest development of artificial intelligence (AI) methodologies and tools, coupled with advancements in large-scale data management and powerful graphic processing computing units, potentiate the integration of these large data sources into relevant biological networks, which will enhance our understanding of cancer MDR. In this review, we revisit common MDR mechanisms and compile a list of the most relevant "omics" public databases. We highlight examples of AI methods that are now decisively contributing to clear advances in cancer research, such as identification of new drugs from large databases and prediction of relevant drug, target, and system properties. An overview of several freely available "ready-to-use" algorithms is also provided. The described molecular scale AI algorithms and tools will undoubtedly guide important improvements in efficiency and efficacy of traditional methods of cancer diagnostics and treatment.

Microglial homeostatic functions are fundamental to regulate the central nervous system microenvironment. We use conditional cell-specific gene targeting, RNA-seq profiling, high-throughput proteomics, phosphoproteomics, systems biology, and animal behavior to report a critical role for the RhoGTPase Rac1 in regulating adult microglia physiology. Ablation of Rac1 in adult microglia impaired their ability to sense and interpret the brain microenvironment and affected their capacity to communicate with synapses to drive cognitive performance, both at the steady-state and during experience-dependent plasticity. Overall, our results reveal a novel and central role for Rac1 as a regulator of microglia homeostasis and a molecular driver of the microglia-synapse crosstalk required for context-dependent sociability and learning related to memory.

The occurrence of endocrine disrupting chemicals (EDCs) is a major issue for marine and coastal environments in the proximity of urban areas. The occurrence of EDCs in the Pearl River Delta region is well documented but specific data related to Macao is unavailable. The levels of bisphenol-A (BPA), estrone (E1), 17α-estradiol (αE2), 17β-estradiol (E2), estriol (E3), and 17α-ethynylestradiol (EE2) were measured in sediment samples collected along the coastline of Macao. BPA was found in all 45 collected samples with lower BPA concentrations associated to the presence of mangrove trees. Biodegradation assays were performed to evaluate the capacity of the microbial communities of the surveyed ecosystems to degrade BPA and its analogue BPS. Using sediments collected at a WWTP discharge point as inoculum, at a concentration of 2 mg l−1 complete removal of BPA was observed within 6 days, whereas for the same concentration BPS removal was of 95% after 10 days, which is particularly interesting since this compound is considered recalcitrant to biodegradation and likely to accumulate in the environment. Supplementation with BPA improved the degradation of bisphenol-S (BPS). Aiming at the isolation of EDCs-degrading bacteria, enrichments were established with sediments supplied with BPA, BPS, E2 and EE2, which led to the isolation of a bacterial strain, identified as Rhodoccoccus sp. ED55, able to degrade the four compounds at different extents. The isolated strain represents a valuable candidate for bioremediation of contaminated soils and waters.

The pollution of water resources by pesticides poses serious problems for public health and the environment. In this study, Actinobacteria strains were isolated from three wastewater treatment plants (WWTPs) and were screened for their ability to degrade 17 pesticide compounds. Preliminary screening of 13 of the isolates of Actinobacteria allowed the selection of 12 strains with potential for the degradation of nine different pesticides as sole carbon source, including aliette, for which there are no previous reports of biodegradation. Evaluation of the bacterial growth and degradation kinetics of the pesticides 2,4-dichlorophenol (2,4-DCP) and thiamethoxam (tiam) by selected Actinobacteria strains was performed in liquid media. Strains Streptomyces sp. ML and Streptomyces sp. OV were able to degrade 45% of 2,4-DCP (50 mg/l) as the sole carbon source in 30 days and 84% of thiamethoxam (35 mg/l) in the presence of 10 mM of glucose in 18 days. The biodegradation of thiamethoxam by Actinobacteria strains was reported for the first time in this study. These strains are promising for use in bioremediation of ecosystems polluted by this type of pesticides.

Membrane proteins (MPs) encompass a large family of proteins with distinct cellular functions, and although representing over 50% of existing pharmaceutical drug targets, their structural and functional information is still very scarce. Over the last years, in silico analysis and algorithm development were essential to characterize MPs and overcome some limitations of experimental approaches. The optimization and improvement of these methods remain an ongoing process, with key advances in MPs' structure, folding, and interface prediction being continuously tackled. Herein, we discuss the latest trends in computational methods toward a deeper understanding of the atomistic and mechanistic details of MPs.

Severe Acute Respiratory Syndrome CoronaVirus-2 (SARS-CoV-2) is composed by four structural proteins and several accessory non-structural proteins. SARS-CoV-2's most abundant structural protein, Membrane (M) protein, has a pivotal role both during viral infection cycle and host interferon antagonism. This is a highly conserved viral protein, thus an interesting and suitable target for drug discovery. In this paper, we explain the structural and dynamic nature of M protein homodimer. To do so, we developed and applied a detailed and robust in silico workflow to predict M protein dimeric structure, membrane orientation, and interface characterization. Single Nucleotide Polymorphisms (SNPs) in M protein were retrieved from over 1.2 M SARS-CoV-2 genomes and proteins from the Global Initiative on Sharing All Influenza Data (GISAID) database, 91 of which were located at the predicted dimer interface. Among those, we identified SNPs in Variants of Concern (VOC) and Variants of Interest (VOI). Binding free energy differences were evaluated for dimer interfacial SNPs to infer mutant protein stabilities. A few high-prevalent mutated residues were found to be especially relevant in VOC and VOI. This realization may be a game changer to structure driven formulation of new therapeutics for SARS-CoV-2.

High-throughput screening technologies continues to produce large amounts of multiomics data from different populations and cell types for various diseases, such as cancer. However, analysis of such data encounters difficulties due to cancer heterogeneity, further exacerbated by human biological complexity and genomic variability. There is a need to redefine the drug discovery development pipeline, bringing an Artificial Intelligence (AI)-powered informational view that integrates relevant biological information and explores new ways to develop effective anticancer approaches. Here, we show SynPred, an interdisciplinary approach that leverages specifically designed ensembles of AI-algorithms, links omics and biophysical traits to predict synergistic anticancer drug synergy. SynPred exhibits state-of-the-art performance metrics: accuracy – 0.85, precision – 0.77, recall – 0.75, AUROC – 0.82, and F1-score - 0.76 in an independent test set. Moreover, data interpretability was achieved by deploying the most current and robust feature importance approaches. A simple web-based application available online at http://www.moreiralab.com/resources/synpred/ was constructed to predict synergistic anticancer drug combinations requiring only the upload of the two drug SMILES to be tested, allowing easy access by non-expert researchers.

Biological therapies, such as recombinant proteins, are nowadays amongst the most promising approaches towards precision medicine. One of the most innovative methodologies currently available aimed at improving the production yield of recombinant proteins with minimization of costs relies on the combination of in silico studies to predict and deepen the understanding of the modified proteins with an experimental approach. The work described herein aims at the design and production of a biomimetic vector containing the single-chain variable domain fragment (scFv) of an anti-HER2 antibody fragment as a targeting motif fused with HIV gp41. Molecular modeling and docking studies were performed to develop the recombinant protein sequence. Subsequently, the DNA plasmid was produced and HEK-293T cells were transfected to evaluate the designed vector. The obtained results demonstrated that the plasmid construction is robust and can be expressed in the selected cell line. The multidisciplinary integrated in silico and experimental strategy adopted for the construction of a recombinant protein which can be used in HER2+-targeted therapy paves the way towards the production of other therapeutic proteins in a more cost-effective way.

Membrane proteins (MPs) encompass a large family of proteins with distinct cellular functions, and although representing over 50% of existing pharmaceutical drug targets, their structural and functional information is still very scarce. Over the last years, in silico analysis and algorithm development were essential to characterize MPs and overcome some limitations of experimental approaches. The optimization and improvement of these methods remain an ongoing process, with key advances in MPs' structure, folding, and interface prediction being continuously tackled. Herein, we discuss the latest trends in computational methods toward a deeper understanding of the atomistic and mechanistic details of MPs.

An aerobic granular sludge (AGS) sequencing batch reactor (SBR) adapted to salinity (12gL-1 NaCl) was operated under alternating anaerobic-aerobic conditions for the treatment of synthetic saline wastewater containing endocrine-disrupting chemicals (EDCs), namely 17estradiol (E2), 17ethinylestradiol (EE2) and bisphenol-A (BPA). The SBR was intermittently fed with the EDCs at 2mgL-1 of each compound. E2 was completely biodegraded, with 60% to 80% removal attained anaerobically and the remaining quickly consumed under aeration. EE2 was sorbed onto the granular sludge biomass in the anaerobic period, but it was desorbed in subsequent cycles even when the compound was not supplied to the reactor. BPA removal was poor but improved after bioaugmentation with an EDCs degrading bacteria. EDCs shock loads did not significantly affect the COD removal nor the activity of ammonium- and nitrite-oxidizing bacteria (AOB and NOB, respectively). In contrast, the activity of phosphate-accumulating organisms (PAOs) was affected, implying a decrease in P removal within the aerobic phase. AGS core microbiome grouped most bacteria belonging to the phylum Proteobacteria, followed by Bacteroidetes. The microbial profile showed that the introduction of the EDCs mixture increased the relative abundance of Chryseobacterium and Flavobacterium. AOB and NOB species were detected in the AGS biomass, with the latter showing lower relative abundance. Different PAOs, such as Rhodocyclus, Tetrasphaera and Gemmatimonas, were also part of the microbial community, but the addition of EDCs decreased significantly the relative abundance of Rhodocyclus. High microbial diversity was sustained over reactor operation, with the main bacterial groups responsible for nutrients and EDCs removal preserved in the AGS system. The results pointed to the maintenance of a core microbiome over reactor operation that may be related to the stability of the AGS process during EDCs loading., This study was supported in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES/Brasil) – Finance Code 001 and the other part was financed by National Funds from Fundação para a Ciência e a Tecnologia (FCT/Portugal) - through the project AGeNT - PTDC/BTA-BTA/31264/2017 (POCI-01-0145-FEDER 031264)and the project CBQF - UID/Multi/50016/2019, info:eu-repo/semantics/publishedVersion

Protein Hot-Spots (HS) are experimentally determined amino acids, key to small ligand binding and tend to be structural landmarks on protein&ndash, protein interactions. As such, they were extensively approached by structure-based Machine Learning (ML) prediction methods. However, the availability of a much larger array of protein sequences in comparison to determined tree-dimensional structures indicates that a sequence-based HS predictor has the potential to be more useful for the scientific community. Herein, we present SPOTONE, a new ML predictor able to accurately classify protein HS via sequence-only features. This algorithm shows accuracy, AUROC, precision, recall and F1-score of 0.82, 0.83, 0.91, 0.82 and 0.85, respectively, on an independent testing set. The algorithm is deployed within a free-to-use webserver at http://moreiralab.com/resources/spotone, only requiring the user to submit a FASTA file with one or more protein sequences.