JAZ is essential for ligand specificity of the COI1/JAZ co-receptor.

Jasmonates are phytohormones that regulate defense and developmental processes in land plants. Despite the chemical diversity of jasmonate ligands in different plant lineages, they are all perceived by COI1/JAZ co-receptor complexes, in which the hormone acts as a molecular glue between the COI1 F-box and a JAZ repressor. It has been shown that COI1 determines ligand specificity based on the receptor crystal structure and the identification of a single COI1 residue, which is responsible for the evolutionary switch in ligand binding. In this work, we show that JAZ proteins contribute to ligand specificity together with COI1. We propose that specific features of JAZ proteins, which are conserved in bryophytes and lycophytes, enable perception of dn-OPDA ligands regardless the size of the COI1 binding pocket. In vascular plant lineages beyond lycophytes, JAZ evolved to limit binding to JA-Ile, thus impeding dn-OPDA recognition by COI1.

Understanding the Interactions of Ubiquitin-Specific Protease 7 with Its Substrates through Molecular Dynamics Simulations: Insights into the Role of Its C-Terminal Domains in Substrate Recognition.

Ubiquitin-specific protease 7 (USP7) is a deubiquitinase enzyme that plays a critical role in regulating various cellular processes by cleaving ubiquitin molecules from target proteins. The C-terminal loop (CTL) motif is a specific region at the C-terminal end of the USP7 enzyme. Recent experiments suggest that the CTL motif plays a role in USP7's catalytic activity by contributing to the enzyme's structural stability, substrate recognition, and catalytic efficiency. The objective of this work is to elucidate these roles through the utilization of computational methods for molecular simulations. For this, we conducted extensive molecular dynamics (MD) simulations to investigate the conformational dynamics and protein-protein interactions within the USP7 enzyme-substrate complex with the substrate consisting of the ubiquitin tagged with the fluorescent label rhodamine 110-gly (Ub-Rho). Our results demonstrate that the CTL motif plays a crucial role in stabilizing the Ubl domains' conformation and augmenting the stability of active conformations within the enzyme-substrate complex. Conversely, the absence of the CTL motif results in increased flexibility and variability in Ubl domains' motion, leading to a reduced percentage of active conformations. Furthermore, our analysis of protein-protein interactions highlights the significance of the CTL motif in anchoring the Ubl45 domains to the catalytic domain (CD), thereby facilitating stable interactions with the substrate. Overall, our findings provide valuable insights into the conformational dynamics and protein-protein interactions inherent in the USP7 enzyme-substrate complex. These insights shed light on some mechanistic details of USP7 concerning the substrate's recognition before its catalytic action.

RCDPeaks: memory-efficient density peaks clustering of long molecular dynamics.

MOTIVATION: Density Peaks is a widely spread clustering algorithm that has been previously applied to Molecular Dynamics (MD) simulations. Its conception of cluster centers as elements displaying both a high density of neighbors and a large distance to other elements of high density, particularly fits the nature of a geometrical converged MD simulation. Despite its theoretical convenience, implementations of Density Peaks carry a quadratic memory complexity that only permits the analysis of relatively short trajectories. RESULTS: Here, we describe DP+, an exact novel implementation of Density Peaks that drastically reduces the RAM consumption in comparison to the scarcely available alternatives designed for MD. Based on DP+, we developed RCDPeaks, a refined variant of the original Density Peaks algorithm. Through the use of DP+, RCDPeaks was able to cluster a one-million frames trajectory using less than 4.5 GB of RAM, a task that would have taken more than 2 TB and about 3× more time with the fastest and less memory-hunger alternative currently available. Other key features of RCDPeaks include the automatic selection of parameters, the screening of center candidates and the geometrical refining of returned clusters. AVAILABILITY AND IMPLEMENTATION: The source code and documentation of RCDPeaks are free and publicly available on GitHub (https://github.com/LQCT/RCDPeaks.git). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MDSCAN: RMSD-based HDBSCAN clustering of long molecular dynamics.

MOTIVATION: The term clustering designates a comprehensive family of unsupervised learning methods allowing to group similar elements into sets called clusters. Geometrical clustering of molecular dynamics (MD) trajectories is a well-established analysis to gain insights into the conformational behavior of simulated systems. However, popular variants collapse when processing relatively long trajectories because of their quadratic memory or time complexity. From the arsenal of clustering algorithms, HDBSCAN stands out as a hierarchical density-based alternative that provides robust differentiation of intimately related elements from noise data. Although a very efficient implementation of this algorithm is available for programming-skilled users (HDBSCAN*), it cannot treat long trajectories under the de facto molecular similarity metric RMSD. RESULTS: Here, we propose MDSCAN, an HDBSCAN-inspired software specifically conceived for non-programmers users to perform memory-efficient RMSD-based clustering of long MD trajectories. Methodological improvements over the original version include the encoding of trajectories as a particular class of vantage-point tree (decreasing time complexity), and a dual-heap approach to construct a quasi-minimum spanning tree (reducing memory complexity). MDSCAN was able to process a trajectory of 1 million frames using the RMSD metric in about 21 h with <8 GB of RAM, a task that would have taken a similar time but more than 32 TB of RAM with the accelerated HDBSCAN* implementation generally used. AVAILABILITY AND IMPLEMENTATION: The source code and documentation of MDSCAN are free and publicly available on GitHub (https://github.com/LQCT/MDScan.git) and as a PyPI package (https://pypi.org/project/mdscan/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BitQT: a graph-based approach to the quality threshold clustering of molecular dynamics.

MOTIVATION: Classical Molecular Dynamics (MD) is a standard computational approach to model time-dependent processes at the atomic level. The inherent sparsity of increasingly huge generated trajectories demands clustering algorithms to reduce other post-simulation analysis complexity. The Quality Threshold (QT) variant is an appealing one from the vast number of available clustering methods. It guarantees that all members of a particular cluster will maintain a collective similarity established by a user-defined threshold. Unfortunately, its high computational cost for processing big data limits its application in the molecular simulation field. RESULTS: In this work, we propose a methodological parallel between QT clustering and another well-known algorithm in the field of Graph Theory, the Maximum Clique Problem. Molecular trajectories are represented as graphs whose nodes designate conformations, while unweighted edges indicate mutual similarity between nodes. The use of a binary-encoded RMSD matrix coupled to the exploitation of bitwise operations to extract clusters significantly contributes to reaching a very affordable algorithm compared to the few implementations of QT for MD available in the literature. Our alternative provides results in good agreement with the exact one while strictly preserving the collective similarity of clusters. AVAILABILITY AND IMPLEMENTATION: The source code and documentation of BitQT are free and publicly available on GitHub (https://github.com/LQCT/BitQT.git) and ReadTheDocs (https://bitqt.readthedocs.io/en/latest/), respectively. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A new era for the design of TRPV1 antagonists and agonists with the use of structural information and molecular docking of capsaicin-like compounds.

The design of TRPV1 antagonists and agonists has reached a new era since TRPV1 structures at near-atomic resolution are available. Today, the ligand-binding forms of several classical antagonists and agonists are known; therefore, the specific role of key TRPV1's residues in binding of ligands can be elucidated. It is possible to place the well-defined pharmacophore of TRPV1 ligands, conformed by head, neck, and tail groups, in the right pocket regions of TRPV1. It will allow a more thorough use of molecular modelling methods to conduct more effective rational drug design protocols. In this work, important points about the interactions between TRPV1 and capsaicin-like compounds are spelled out, based on the known pharmacophore of the ligands and the already available TRPV1 structures. These points must be addressed to generate reliable poses of novel candidates and should be considered during the design of novel TRPV1 antagonists and agonists.

LigRMSD: a web server for automatic structure matching and RMSD calculations among identical and similar compounds in protein-ligand docking.

MOTIVATION: Root mean square deviation (RMSD) is one of the most useful and straightforward features for structural comparison between different conformations of the same molecule. Commonly, protein-ligand docking programs have included some utilities that allow the calculation of this value; however, they only work efficiently when exists a complete atom label equivalence between the evaluated conformations. RESULTS: We present LigRMSD, a free web-server for the automatic matching and RMSD calculations among identical or similar chemical compounds. This server allows the user to submit only a pair of identical or similar molecules or dataset of similar compounds to compare their three-dimensional conformations. AVAILABILITY AND IMPLEMENTATION: LigRMSD can be freely accessed at https://ligrmsd.appsbio.utalca.cl. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PSIQUE: Protein Secondary Structure Identification on the Basis of Quaternions and Electronic Structure Calculations.

The secondary structure is important in protein structure analysis, classification, and modeling. We have developed a novel method for secondary structure assignment, termed PSIQUE, based on the potential energy surface (PES) of polyalanine obtained using an infinitely long chain model and density functional theory calculations. First, uniform protein segments are determined in terms of a difference of quaternions between neighboring amino acids along the protein backbone. Then, the identification of the secondary structure motifs is carried out based on the minima found in the PES. PSIQUE shows good agreement with other secondary structure assignment methods. However, it provides better discrimination of subtle secondary structures (e.g., helix types) and termini and produces more uniform segments while also accounting for local distortions. Overall, PSIQUE provides a precise and reliable assignment of secondary structures, so it should be helpful for the detailed characterization of the protein structure.

Dammarane triterpenes targeting α-synuclein: biological activity and evaluation of binding sites by molecular docking.

Parkinson's disease (PD) is a neurodegenerative disorder that affects adult people whose treatment is palliative. Thus, we decided to test three dammarane triterpenes 1, 1a, 1b, and we determined that 1 and 1a inhibit ß-aggregation through thioflavine T rather than 1b. Since compound 1 was most active, we determined the interaction between α-synuclein and 1 at 50 µM (Kd) through microscale thermophoresis. Also, we observed differences in height and diameter of aggregates, and α-synuclein remains unfolded in the presence of 1. Also, aggregates treated with 1 do not provoke neurites' retraction in N2a cells previously induced by retinoic acid. Finally, we studied the potential sites of interaction between 1 with α-synuclein fibrils using molecular modelling. Docking experiments suggest that 1 preferably interact with the site 2 of α-synuclein through hydrogen bonds with residues Y39 and T44.

Computational Modeling to Explain Why 5,5-Diarylpentadienamides are TRPV1 Antagonists.

Several years ago, the crystallographic structures of the transient receptor potential vanilloid 1 (TRPV1) in the presence of agonists and antagonists were reported, providing structural information about its chemical activation and inactivation. TRPV1's activation increases the transport of calcium and sodium ions, leading to the excitation of sensory neurons and the perception of pain. On the other hand, its antagonistic inactivation has been explored to design analgesic drugs. The interactions between the antagonists 5,5-diarylpentadienamides (DPDAs) and TRPV1 were studied here to explain why they inactivate TRPV1. The present work identified the structural features of TRPV1-DPDA complexes, starting with a consideration of the orientations of the ligands inside the TRPV1 binding site by using molecular docking. After this, a chemometrics analysis was performed (i) to compare the orientations of the antagonists (by using LigRMSD), (ii) to describe the recurrent interactions between the protein residues and ligand groups in the complexes (by using interaction fingerprints), and (iii) to describe the relationship between topological features of the ligands and their differential antagonistic activities (by using a quantitative structure-activity relationship (QSAR) with 2D autocorrelation descriptors). The interactions between the DPDA groups and the residues Y511, S512, T550, R557, and E570 (with a recognized role in the binding of classic ligands), and the occupancy of isoquinoline or 3-hydroxy-3,4-dihydroquinolin-2(1H)-one groups of the DPDAs in the vanilloid pocket of TRPV1 were clearly described. Based on the results, the structural features that explain why DPDAs inactivate TRPV1 were clearly exposed. These features can be considered for the design of novel TRPV1 antagonists.

Multi-scale simulation reveals that an amino acid substitution increases photosensitizing reaction inputs in Rhodopsins.

Evaluating the availability of molecular oxygen (O2 ) and energy of excited states in the retinal binding site of rhodopsin is a crucial challenging first step to understand photosensitizing reactions in wild-type (WT) and mutant rhodopsins by absorbing visible light. In the present work, energies of the ground and excited states related to 11-cis-retinal and the O2 accessibility to the ß-ionone ring are evaluated inside WT and human M207R mutant rhodopsins. Putative O2 pathways within rhodopsins are identified by using molecular dynamics simulations, Voronoi-diagram analysis, and implicit ligand sampling while retinal energetic properties are investigated through density functional theory, and quantum mechanical/molecular mechanical methods. Here, the predictions reveal that an amino acid substitution can lead to enough energy and O2 accessibility in the core hosting retinal of mutant rhodopsins to favor the photosensitized singlet oxygen generation, which can be useful in understanding retinal degeneration mechanisms and in designing blue-lighting-absorbing proteic photosensitizers.

A genome-wide association analysis reveals a potential role for recombination in the evolution of antimicrobial resistance in Burkholderia multivorans.

Cystic fibrosis (CF) lung infections caused by members of the Burkholderia cepacia complex, such as Burkholderia multivorans, are associated with high rates of mortality and morbidity. We performed a population genomics study of 111 B. multivorans sputum isolates from one CF patient through three stages of infection including an early incident isolate, deep sampling of a one-year period of chronic infection occurring weeks before a lung transplant, and deep sampling of a post-transplant infection. We reconstructed the evolutionary history of the population and used a lineage-controlled genome-wide association study (GWAS) approach to identify genetic variants associated with antibiotic resistance. We found the incident isolate was basally related to the rest of the strains and more susceptible to antibiotics from three classes (ß-lactams, aminoglycosides, quinolones). The chronic infection isolates diversified into multiple, distinct genetic lineages and showed reduced antimicrobial susceptibility to the same antibiotics. The post-transplant reinfection isolates derived from the same source as the incident isolate and were genetically distinct from the chronic isolates. They also had a level of susceptibility in between that of the incident and chronic isolates. We identified numerous examples of potential parallel pathoadaptation, in which multiple mutations were found in the same locus or even codon. The set of parallel pathoadaptive loci was enriched for functions associated with virulence and resistance. Our GWAS analysis identified statistical associations between a polymorphism in the ampD locus with resistance to ß-lactams, and polymorphisms in an araC transcriptional regulator and an outer membrane porin with resistance to both aminoglycosides and quinolones. Additionally, these three loci were independently mutated four, three and two times, respectively, providing further support for parallel pathoadaptation. Finally, we identified a minimum of 14 recombination events, and observed that loci carrying putative parallel pathoadaptations and polymorphisms statistically associated with ß-lactam resistance were over-represented in these recombinogenic regions.

Quality Threshold Clustering of Molecular Dynamics: A Word of Caution.

Clustering Molecular Dynamics trajectories is a common analysis that allows grouping together similar conformations. Several algorithms have been designed and optimized to perform this routine task, and among them, Quality Threshold stands as a very attractive option. This algorithm guarantees that in retrieved clusters no pair of frames will have a similarity value greater than a specified threshold, and hence, a set of strongly correlated frames are obtained for each cluster. In this work, it is shown that various commonly used software implementations are flawed by confusing Quality Threshold with another simplistic well-known clustering algorithm published by Daura et al. (Daura, X.; van Gunsteren, W. F.; Jaun, B.; Mark, A. E.; Gademann, K.; Seebach, D. Peptide Folding: When Simulation Meets Experiment. Angew. Chemie Int. Ed. 1999, 38 (1/2), 236-240). Daura's algorithm does not impose any quality threshold for the frames contained in retrieved clusters, bringing unrelated structural configurations altogether. The advantages of using Quality Threshold whenever possible to explore Molecular Dynamic trajectories is exemplified. An in-house implementation of the original Quality Threshold algorithm has been developed in order to illustrate our comments, and its code is freely available for further use by the scientific community.

Coarse-Grained Parameters for Divalent Cations within the SIRAH Force Field.

Although molecular dynamics simulations allow for the study of interactions among virtually all biomolecular entities, metal ions still pose significant challenges in achieving an accurate structural and dynamical description of many biological assemblies, particularly to coarse-grained (CG) models. Although the reduced computational cost of CG methods often makes them the technique of choice for the study of large biomolecular systems, the parameterization of metal ions is still very crude or not available for the vast majority of CG force fields. Here, we show that incorporating statistical data retrieved from the Protein Data Bank (PDB) to set specific Lennard-Jones interactions can produce structurally accurate CG molecular dynamics simulations using the SIRAH force field. We provide a set of interaction parameters for calcium, magnesium, and zinc ions, which cover more than 80% of the metal-bound structures reported in the PDB. Simulations performed on several proteins and DNA systems show that it is possible to preclude the use of topological constraints by modifying specific Lennard-Jones interactions.

BitClust: Fast Geometrical Clustering of Long Molecular Dynamics Simulations.

The growing computational capacity allows the investigation of large biomolecular systems by increasingly extensive molecular dynamics simulations. The resulting huge trajectories demand efficient partition methods to discern relevant structural dissimilarity. Clustering algorithms are available to address this task, but their implementations still need to be improved to gain in computational speed and to reduce the consumption of random access memory. We propose the BitClust code which, based on a combination of Python and C programming languages, performs fast structural clustering of long molecular trajectories. BitClust takes advantage of bitwise operations applied to a bit-encoded pairwise similarity matrix. Our approach allowed us to process a half-million frame trajectory in 6 h using less than 35 GB, a task that is not affordable with any of the similar alternatives.

New Insights into the Opening of the Occluded Ligand-Binding Pocket of Sigma1 Receptor: Binding of a Novel Bivalent RC-33 Derivative.

Significant progresses have been made to understand the molecular basis of the Sigma1 receptor (S1R) operating in normal and pathological conditions. S1R is a transmembrane protein that participates in a wide variety of processes at the central nervous system; hence, its function has been associated with mental and neurological disorders. Several ligands have been proposed to regulate the function of S1R revealing a high plasticity of the ligand-binding pocket. Previous drug-design studies have been mainly based on pharmacophore models; however, the recently revealed crystal structure of S1R provides an excellent opportunity for verifying previous predictions and for evaluating the binding of novel compounds. Interestingly, the crystal structure shows that the binding pocket of S1R is highly occluded from solvent; therefore, it is not clear how ligands access this site. In the present work, we applied steered molecular dynamics (SMD) simulations to open the occluded ligand-binding pocket in the S1R crystal structure and to determine the preferred ligand pathway to enter and exit the binding site. The intracellular surface of the ß-barrel ligand-binding region was found the most favorable route to accommodate ligands. This route supports the binding of RC-33 (our in-house-developed S1R modulator) and a new bivalent derivative that constitutes the first divalent structure shown to interact with S1R. Free energy calculations of these compounds associated with S1R agree with experimental Ki values and provide molecular insights of the binding mode of modulators that could access the S1R ligand-binding pocket through the cytoplasmic region.

Identification of Mycobacterium tuberculosis CtpF as a target for designing new antituberculous compounds.

The emergence of tuberculosis (TB) produced by multi-drug resistance (MDR) and extensively-drug resistance (XDR) Mycobacterium tuberculosis (Mtb), encourages the development of new antituberculous compounds, as well as the identification of novel drug targets. In this regard, plasma membrane P-type ATPases are interesting targets because they play a crucial role in ion homeostasis and mycobacterial survival. We focused on Mtb CtpF, a calcium P-type ATPase that responds to a broad number of intraphagosomal conditions, as a novel target. In this study, we evaluated the capacity of cyclopiazonic acid (CPA), a well-known inhibitor of the sarco-endoplasmic reticulum Ca2+-ATPase (SERCA), to inhibit the ATPase activity of CtpF and the Mtb growth demonstrating that CtpF is a druggable target. A homology modeling of CtpF was generated for molecular docking studies of CtpF with CPA and key pharmacophoric features were identified, which were used to perform a pharmacophore-based virtual screening of the ZINC database, and to identify CtpF inhibitor candidates. Molecular docking-based virtual screening and MM-BGSA calculations of candidates allowed identifying six compounds with the best binding energies. The compounds displayed in vitro minimum inhibitory concentrations (MIC) ranging from 50 to 100 µg/mL, growth inhibitions from 29.5 to 64.0% on Mtb, and inhibitions of Ca2+-dependent ATPase activity in Mtb membrane vesicles (IC50) ranging from 4.1 to 35.8 µM. The compound ZINC63908257 was the best candidate by displaying a MIC of 50 µg/mL and a Ca2+ P-type ATPase inhibition of 45% with IC50 = 4.4 µM. Overall, the results indicate that CtpF is a druggable target for designing new antituberculous compounds.

Considerations for Docking of Selective Angiotensin-Converting Enzyme Inhibitors.

The angiotensin-converting enzyme (ACE) is a two-domain dipeptidylcarboxypeptidase, which has a direct involvement in the control of blood pressure by performing the hydrolysis of angiotensin I to produce angiotensin II. At the same time, ACE hydrolyzes other substrates such as the vasodilator peptide bradykinin and the anti-inflammatory peptide N-acetyl-SDKP. In this sense, ACE inhibitors are bioactive substances with potential use as medicinal products for treatment or prevention of hypertension, heart failures, myocardial infarction, and other important diseases. This review examined the most recent literature reporting ACE inhibitors with the help of molecular modeling. The examples exposed here demonstrate that molecular modeling methods, including docking, molecular dynamics (MD) simulations, quantitative structure-activity relationship (QSAR), etc, are essential for a complete structural picture of the mode of action of ACE inhibitors, where molecular docking has a key role. Examples show that too many works identified ACE inhibitory activities of natural peptides and peptides obtained from hydrolysates. In addition, other works report non-peptide compounds extracted from natural sources and synthetic compounds. In all these cases, molecular docking was used to provide explanation of the chemical interactions between inhibitors and the ACE binding sites. For docking applications, most of the examples exposed here do not consider that: (i) ACE has two domains (nACE and cACE) with available X-ray structures, which are relevant for the design of selective inhibitors, and (ii) nACE and cACE binding sites have large dimensions, which leads to non-reliable solutions during docking calculations. In support of the solution of these problems, the structural information found in Protein Data Bank (PDB) was used to perform an interaction fingerprints (IFPs) analysis applied on both nACE and cACE domains. This analysis provides plots that identify the chemical interactions between ligands and both ACE binding sites, which can be used to guide docking experiments in the search of selective natural components or novel drugs. In addition, the use of hydrogen bond constraints in the S2 and S2' subsites of nACE and cACE are suggested to guarantee that docking solutions are reliable.

Structural and biochemical approaches uncover multiple evolutionary trajectories of plant quinate dehydrogenases.

Quinate is produced and used by many plants in the biosynthesis of chlorogenic acids (CGAs). Chlorogenic acids are astringent and serve to deter herbivory. They also function as antifungal agents and have potent antioxidant properties. Quinate is produced at a branch point of shikimate biosynthesis by the enzyme quinate dehydrogenase (QDH). However, little information exists on the identity and biochemical properties of plant QDHs. In this study, we utilized structural and bioinformatics approaches to establish a QDH-specific primary sequence motif. Using this motif, we identified QDHs from diverse plants and confirmed their activity by recombinant protein production and kinetic assays. Through a detailed phylogenetic analysis, we show that plant QDHs arose directly from bifunctional dehydroquinate dehydratase-shikimate dehydrogenases (DHQD-SDHs) through different convergent evolutionary events, illustrated by our findings that eudicot and conifer QDHs arose early in vascular plant evolution whereas Brassicaceae QDHs emerged later. This process of recurrent evolution of QDH is further demonstrated by the fact that this family of proteins independently evolved NAD+ and NADP+ specificity in eudicots. The acquisition of QDH activity by these proteins was accompanied by the inactivation or functional evolution of the DHQD domain, as verified by enzyme activity assays and as reflected in the loss of key DHQD active site residues. The implications of QDH activity and evolution are discussed in terms of plant growth and development.

Structural Requirements of N-alpha-Mercaptoacetyl Dipeptide (NAMdP) Inhibitors of Pseudomonas Aeruginosa Virulence Factor LasB: 3D-QSAR, Molecular Docking, and Interaction Fingerprint Studies.

The zinc metallopeptidase Pseudomonas elastase (LasB) is a virulence factor of Pseudomonas aeruginosa (P. aeruginosa), a pathogenic bacterium that can cause nosocomial infections. The present study relates the structural analysis of 118 N-alpha-mercaptoacetyl dipeptides (NAMdPs) as LasB inhibitors. Field-based 3D-QSAR and molecular docking methods were employed to describe the essential interactions between NAMdPs and LasB binding sites, and the chemical features that determine their differential activities. We report a predictive 3D-QSAR model that was developed according to the internal and external validation tests. The best model, including steric, electrostatic, hydrogen bond donor, hydrogen bond acceptor, and hydrophobic fields, was found to depict a three-dimensional map with the local positive and negative effects of these chemotypes on the LasB inhibitory activities. Furthermore, molecular docking experiments yielded bioactive conformations of NAMdPs inside the LasB binding site. The series of NAMdPs adopted a similar orientation with respect to phosphoramidon within the LasB binding site (crystallographic reference), where the backbone atoms of NAMdPs are hydrogen-bonded to the LasB residues N112, A113, and R198, similarly to phosphoramidon. Our study also included a deep description of the residues involved in the protein-ligand interaction patterns for the whole set of NAMdPs, through the use of interaction fingerprints (IFPs).