The SAMPL9 host-guest blind challenge: an overview of binding free energy predictive accuracy.

We report the results of the SAMPL9 host-guest blind challenge for predicting binding free energies. The challenge focused on macrocycles from pillar[n]-arene and cyclodextrin host families, including WP6, and bCD and HbCD. A variety of methods were used by participants to submit binding free energy predictions. A machine learning approach based on molecular descriptors achieved the highest accuracy (RMSE of 2.04 kcal mol-1) among the ranked methods in the WP6 dataset. Interestingly, predictions for WP6 obtained via docking tended to outperform all methods (RMSE of 1.70 kcal mol-1), most of which are MD based and computationally more expensive. In general, methods applying force fields achieved better correlation with experiments for WP6 opposed to the machine learning and docking models. In the cyclodextrin-phenothiazine challenge, the ATM approach emerged as the top performing method with RMSE less than 1.86 kcal mol-1. Correlation metrics of ranked methods in this dataset were relatively poor compared to WP6. We also highlight several lessons learned to guide future work and help improve studies on the systems discussed. For example, WP6 may be present in other microstates other than its -12 state in the presence of certain guests. Machine learning approaches can be used to fine tune or help train force fields for certain chemistry (i.e. WP6-G4). Certain phenothiazines occupy distinct primary and secondary orientations, some of which were considered individually for accurate binding free energies. The accuracy of predictions from certain methods while starting from a single binding pose/orientation demonstrates the sensitivity of calculated binding free energies to the orientation, and in some cases the likely dominant orientation for the system. Computational and experimental results suggest that guest phenothiazine core traverses both the secondary and primary faces of the cyclodextrin hosts, a bulky cationic side chain will primarily occupy the primary face, and the phenothiazine core substituent resides at the larger secondary face.

An overview of the SAMPL8 host-guest binding challenge.

The SAMPL series of challenges aim to focus the community on specific modeling challenges, while testing and hopefully driving progress of computational methods to help guide pharmaceutical drug discovery. In this study, we report on the results of the SAMPL8 host-guest blind challenge for predicting absolute binding affinities. SAMPL8 focused on two host-guest datasets, one involving the cucurbituril CB8 (with a series of common drugs of abuse) and another involving two different Gibb deep-cavity cavitands. The latter dataset involved a previously featured deep cavity cavitand (TEMOA) as well as a new variant (TEETOA), both binding to a series of relatively rigid fragment-like guests. Challenge participants employed a reasonably wide variety of methods, though many of these were based on molecular simulations, and predictive accuracy was mixed. As in some previous SAMPL iterations (SAMPL6 and SAMPL7), we found that one approach to achieve greater accuracy was to apply empirical corrections to the binding free energy predictions, taking advantage of prior data on binding to these hosts. Another approach which performed well was a hybrid MD-based approach with reweighting to a force matched QM potential. In the cavitand challenge, an alchemical method using the AMOEBA-polarizable force field achieved the best success with RMSE less than 1 kcal/mol, while another alchemical approach (ATM/GAFF2-AM1BCC/TIP3P/HREM) had RMSE less than 1.75 kcal/mol. The work discussed here also highlights several important lessons; for example, retrospective studies of reference calculations demonstrate the sensitivity of predicted binding free energies to ethyl group sampling and/or guest starting pose, providing guidance to help improve future studies on these systems.

Rapid Identification of Drug Resistance and Phylogeny in M. tuberculosis, Directly from Sputum Samples.

Tuberculosis (TB) remains one of the most important infectious diseases globally. Establishing a resistance profile from the initial TB diagnosis is a priority. Rapid molecular tests evaluate only the most common genetic variants responsible for resistance to certain drugs, and Whole Genome Sequencing (WGS) needs culture prior to next-generation sequencing (NGS), limiting their clinical value. Targeted sequencing (TS) from clinical samples avoids these drawbacks, providing a signature of genetic markers that can be associated with drug resistance and phylogeny. In this study, a proof-of-concept protocol was developed for detecting genomic variants associated with drug resistance and for the phylogenetic classification of Mycobacterium Tuberculosis (Mtb) in sputum samples. Initially, a set of Mtb reference strains from the WHO were sequenced (WGS and TS). The results from the protocol agreed >95% with WHO reported data and phenotypic drug susceptibility testing (pDST). Lineage genetics results were 100% concordant with those derived from WGS. After that, the TS protocol was applied to sputum samples from TB patients to detect resistance to first- and second-line drugs and derive phylogeny. The accuracy was >90% for all evaluated drugs, except Eto/Pto (77.8%), and 100% were phylogenetically classified. The results indicate that the described protocol, which affords the complete drug resistance profile and phylogeny of Mtb from sputum, could be useful in the clinical area, advancing toward more personalized and more effective treatments in the near future. IMPORTANCE The COVID-19 pandemic negatively affected the progress in accessing essential Tuberculosis (TB) services and reducing the burden of TB disease, resulting in a decreased detection of new cases and increased deaths. Generating molecular diagnostic tests with faster results without losing reliability is considered a priority. Specifically, developing an antimicrobial resistance profile from the initial stages of TB diagnosis is essential to ensure appropriate treatment. Currently available rapid molecular tests evaluate only the most common genetic variants responsible for resistance to certain drugs, limiting their clinical value. In this work, targeted sequencing on sputum samples from TB patients was used to identify Mycobacterium tuberculosis mutations in genes associated with drug resistance and to derive a phylogeny of the infecting strain. This protocol constitutes a proof-of-concept toward the goal of helping clinicians select a timely and appropriate treatment by providing them with actionable information beyond current molecular approaches.

The Microbiome as Part of the Contemporary View of Tuberculosis Disease.

The study of the microbiome has changed our overall perspective on health and disease. Although studies of the lung microbiome have lagged behind those on the gastrointestinal microbiome, there is now evidence that the lung microbiome is a rich, dynamic ecosystem. Tuberculosis is one of the oldest human diseases, it is primarily a respiratory infectious disease caused by strains from the Mycobacterium tuberculosis Complex. Even today, during the COVID-19 pandemic, it remains one of the principal causes of morbidity and mortality worldwide. Tuberculosis disease manifests itself as a dynamic spectrum that ranges from asymptomatic latent infection to life-threatening active disease. The review aims to provide an overview of the microbiome in the tuberculosis setting, both in patients' and animal models. We discuss the relevance of the microbiome and its dysbiosis, and how, probably through its interaction with the immune system, it is a significant factor in tuberculosis's susceptibility, establishment, and severity.

A bioinformatics pipeline for Mycobacterium tuberculosis sequencing that cleans contaminant reads from sputum samples.

Next-Generation Sequencing (NGS) is widely used to investigate genomic variation. In several studies, the genetic variation of Mycobacterium tuberculosis has been analyzed in sputum samples without previous culture, using target enrichment methodologies for NGS. Alignments obtained by different programs generally map the sequences under default parameters, and from these results, it is assumed that only Mycobacterium reads will be obtained. However, variants of interest microorganism in clinical samples can be confused with a vast collection of reads from other bacteria, viruses, and human DNA. Currently, there are no standardized pipelines, and the cleaning success is never verified since there is a lack of rigorous controls to identify and remove reads from other sputum-microorganisms genetically similar to M. tuberculosis. Therefore, we designed a bioinformatic pipeline to process NGS data from sputum samples, including several filters and quality control points to identify and eliminate non-M. tuberculosis reads to obtain a reliable genetic variant report. Our proposal uses the SURPI software as a taxonomic classifier to filter input sequences and perform a mapping that provides the highest percentage of Mycobacterium reads, minimizing the reads from other microorganisms. We then use the filtered sequences to perform variant calling with the GATK software, ensuring the mapping quality, realignment, recalibration, hard-filtering, and post-filter to increase the reliability of the reported variants. Using default mapping parameters, we identified reads of contaminant bacteria, such as Streptococcus, Rhotia, Actinomyces, and Veillonella. Our final mapping strategy allowed a sequence identity of 97.8% between the input reads and the whole M. tuberculosis reference genome H37Rv using a genomic edit distance of three, thus removing 98.8% of the off-target sequences with a Mycobacterium reads loss of 1.7%. Finally, more than 200 unreliable genetic variants were removed during the variant calling, increasing the report's reliability.

Discovery of Dipeptides as Potent Botulinum Neurotoxin A Light-Chain Inhibitors.

The botulinum neurotoxin, the caustic agent that causes botulism, is the most lethal toxin known to man. The neurotoxin composed of a heavy chain (HC) and a light chain (LC) enters neurons and cleaves SNARE proteins, leading to flaccid paralysis, which, in severe occurrences, can result in death. A therapeutic target for botulinum neurotoxin (BoNT) intoxication is the LC, a zinc metalloprotease that directly cleaves SNARE proteins. Herein we report dipeptides containing an aromatic connected to the N-terminus via a sulfonamide and a hydroxamic acid at the C-terminus as BoNT/A LC inhibitors. On the basis of a structure-activity relationship study, 33 was discovered to inhibit the BoNT/A LC with an IC50 of 21 nM. X-ray crystallography analysis of 30 and 33 revealed that the dipeptides inhibit through a competitive mechanism and identified several key intermolecular interactions.

SAMPL7 Host-Guest Challenge Overview: assessing the reliability of polarizable and non-polarizable methods for binding free energy calculations.

The SAMPL challenges focus on testing and driving progress of computational methods to help guide pharmaceutical drug discovery. However, assessment of methods for predicting binding affinities is often hampered by computational challenges such as conformational sampling, protonation state uncertainties, variation in test sets selected, and even lack of high quality experimental data. SAMPL blind challenges have thus frequently included a component focusing on host-guest binding, which removes some of these challenges while still focusing on molecular recognition. Here, we report on the results of the SAMPL7 blind prediction challenge for host-guest affinity prediction. In this study, we focused on three different host-guest categories-a familiar deep cavity cavitand series which has been featured in several prior challenges (where we examine binding of a series of guests to two hosts), a new series of cyclodextrin derivatives which are monofunctionalized around the rim to add amino acid-like functionality (where we examine binding of two guests to a series of hosts), and binding of a series of guests to a new acyclic TrimerTrip host which is related to previous cucurbituril hosts. Many predictions used methods based on molecular simulations, and overall success was mixed, though several methods stood out. As in SAMPL6, we find that one strategy for achieving reasonable accuracy here was to make empirical corrections to binding predictions based on previous data for host categories which have been studied well before, though this can be of limited value when new systems are included. Additionally, we found that alchemical free energy methods using the AMOEBA polarizable force field had considerable success for the two host categories in which they participated. The new TrimerTrip system was also found to introduce some sampling problems, because multiple conformations may be relevant to binding and interconvert only slowly. Overall, results in this challenge tentatively suggest that further investigation of polarizable force fields for these challenges may be warranted.

Synthesis and activity of isoleucine sulfonamide derivatives as novel botulinum neurotoxin serotype A light chain inhibitors.

The botulinum neurotoxin (BoNT) is the most lethal protein known to man causing the deadly disease botulinum. The neurotoxin, composed of a heavy (HC) and light (LC) chain, work in concert to cause muscle paralysis. A therapeutic strategy to treat individuals infected with the neurotoxin is inhibiting the catalytic activity of the BoNT LC. We report the synthesis, inhibition study and computational docking analysis of novel small molecule BoNT/A LC inhibitors. A structure activity relationship study resulted in the discovery of d-isoleucine functionalized with a hydroxamic acid on the C-terminal and a biphenyl with chlorine at C- 2 connected by a sulfonamide linker at the N-terminus. This compound has a measured IC50 of 0.587 µM for the BoNT/A LC. Computational docking analysis indicates the sulfonamide linker adopts a geometry that is advantageous for binding to the BoNT LC active site. In addition, Arg363 is predicted to be involved in key binding interactions with the scaffold in this study.

Characterization of Polymorphisms Associated with Multidrug-Resistant Tuberculosis by Whole Genomic Sequencing: A Preliminary Report from Mexico.

Whole genome sequencing (WGS) has been proposed as a tool for the diagnosis of drug resistance in tuberculosis (TB); however, there have been few studies on its effectiveness in countries with significantly high drug resistance rates. This study therefore aimed to evaluate the effectiveness of WGS to identify mutations related to drug resistance in TB isolates from an endemic region of Mexico. The results showed that, of 35 multidrug-resistant isolates analyzed, the values of congruence found between the phenotypic drug susceptibility testing and polymorphisms were 94% for isoniazid, 97% for rifampicin, 90% for ethambutol, and 82% for pyrazinamide. It was also possible to identify eight isolates as potential pre-extensive drug resistant (XDR) and one as XDR. Twenty nine isolates were classified within L4 and two transmission clusters were identified. The results show the potential utility of WGS for predicting resistance against first- and second-line drugs, as well as providing a phylogenetic characterization of TB drug-resistant isolates circulating in Mexico.

Genomic diversity of prevalent Staphylococcus epidermidis multidrug-resistant strains isolated from a Children's Hospital in México City in an eight-years survey.

Staphylococcus epidermidis is a human commensal and pathogen worldwide distributed. In this work, we surveyed for multi-resistant S. epidermidis strains in eight years at a children's health-care unit in México City. Multidrug-resistant S. epidermidis were present in all years of the study, including resistance to methicillin, beta-lactams, fluoroquinolones, and macrolides. To understand the genetic basis of antibiotic resistance and its association with virulence and gene exchange, we sequenced the genomes of 17 S. epidermidis isolates. Whole-genome nucleotide identities between all the pairs of S. epidermidis strains were about 97% to 99%. We inferred a clonal structure and eight Multilocus Sequence Types (MLSTs) in the S. epidermidis sequenced collection. The profile of virulence includes genes involved in biofilm formation and phenol-soluble modulins (PSMs). Half of the S. epidermidis analyzed lacked the ica operon for biofilm formation. Likely, they are commensal S. epidermidis strains but multi-antibiotic resistant. Uneven distribution of insertion sequences, phages, and CRISPR-Cas immunity phage systems suggest frequent horizontal gene transfer. Rates of recombination between S. epidermidis strains were more prevalent than the mutation rate and affected the whole genome. Therefore, the multidrug resistance, independently of the pathogenic traits, might explain the persistence of specific highly adapted S. epidermidis clonal lineages in nosocomial settings.

Whole genomic sequencing as a tool for diagnosis of drug and multidrug-resistance tuberculosis in an endemic region in Mexico.

BACKGROUND: Whole genome sequencing (WGS) has been proposed as a tool for diagnosing drug resistance in tuberculosis. However, reports of its effectiveness in endemic countries with important numbers of drug resistance are scarce. The goal of this study was to evaluate the effectiveness of this procedure in isolates from a tuberculosis endemic region in Mexico. METHODS: WGS analysis was performed in 81 tuberculosis positive clinical isolates with a known phenotypic profile of resistance against first-line drugs (isoniazid, rifampin, ethambutol, pyrazinamide and streptomycin). Mutations related to drug resistance were identified for each isolate; drug resistant genotypes were predicted and compared with the phenotypic profile. Genotypes and transmission clusters based on genetic distances were also characterized. FINDINGS: Prediction by WGS analysis of resistance against isoniazid, rifampicin, ethambutol, pyrazinamide and streptomycin showed sensitivity values of 84%, 96%, 71%, 75% and 29%, while specificity values were 100%, 94%, 90%, 90% and 98%, respectively. Prediction of multidrug resistance showed a sensitivity of 89% and specificity of 97%. Moreover, WGS analysis revealed polymorphisms related to second-line drug resistance, enabling classification of eight and two clinical isolates as pre- and extreme drug-resistant cases, respectively. Lastly, four lineages were identified in the population (L1, L2, L3 and L4). The most frequent of these was L4, which included 90% (77) of the isolates. Six transmission clusters were identified; the most frequent was TC6, which included 13 isolates with a L4.1.1 and a predominantly multidrug-resistant condition. CONCLUSIONS: The results illustrate the utility of WGS for establishing the potential for prediction of resistance against first and second line drugs in isolates of tuberculosis from the region. They also demonstrate the feasibility of this procedure for use as a tool to support the epidemiological surveillance of drug- and multidrug-resistant tuberculosis.