ATOMDANCE: Kernel-based denoising and choreographic analysis for protein dynamic comparison.

Comparative methods in molecular evolution and structural biology rely heavily upon the site-wise analysis of DNA sequence and protein structure, both static forms of information. However, it is widely accepted that protein function results from nanoscale nonrandom machine-like motions induced by evolutionarily conserved molecular interactions. Comparisons of molecular dynamics (MD) simulations conducted between homologous sites representative of different functional or mutational states can potentially identify local effects on binding interaction and protein evolution. In addition, comparisons of different (i.e., nonhomologous) sites within MD simulations could be employed to identify functional shifts in local time-coordinated dynamics indicative of logic gating within proteins. However, comparative MD analysis is challenged by the large fraction of protein motion caused by random thermal noise in the surrounding solvent. Therefore, properly denoised MD comparisons could reveal functional sites involving these machine-like dynamics with good accuracy. Here, we introduce ATOMDANCE, a user-interfaced suite of comparative machine learning-based denoising tools designed for identifying functional sites and the patterns of coordinated motion they can create within MD simulations. ATOMDANCE-maxDemon4.0 employs Gaussian kernel functions to compute site-wise maximum mean discrepancy between learned features of motion, thereby assessing denoised differences in the nonrandom motions between functional or evolutionary states (e.g., ligand bound versus unbound, wild-type versus mutant). ATOMDANCE-maxDemon4.0 also employs maximum mean discrepancy to analyze potential random amino acid replacements allowing for a site-wise test of neutral versus nonneutral evolution on the divergence of dynamic function in protein homologs. Finally, ATOMDANCE-Choreograph2.0 employs mixed-model analysis of variance and graph network to detect regions where time-synchronized shifts in dynamics occur. Here, we demonstrate ATOMDANCE's utility for identifying key sites involved in dynamic responses during functional binding interactions involving DNA, small-molecule drugs, and virus-host recognition, as well as understanding shifts in global and local site coordination occurring during allosteric activation of a pathogenic protease.

Rolling the evolutionary dice: Neisseria commensals as proxies for elucidating the underpinnings of antibiotic resistance mechanisms and evolution in human pathogens.

Species within the genus Neisseria are adept at sharing adaptive allelic variation, with commensal species repeatedly transferring resistance to their pathogenic relative Neisseria gonorrhoeae. However, resistance in commensals is infrequently characterized, limiting our ability to predict novel and potentially transferable resistance mechanisms that ultimately may become important clinically. Unique evolutionary starting places of each Neisseria species will have distinct genomic backgrounds, which may ultimately control the fate of evolving populations in response to selection as epistatic and additive interactions coerce lineages along divergent evolutionary trajectories. Alternatively, similar genetic content present across species due to shared ancestry may constrain existing adaptive solutions. Thus, identifying the paths to resistance across commensals may aid in characterizing the Neisseria resistome-or the reservoir of alleles within the genus as well as its depth. Here, we use in vitro evolution of four commensal species to investigate the potential and repeatability of resistance evolution to two antimicrobials, the macrolide azithromycin and the ß-lactam penicillin. After 20 days of selection, commensals evolved resistance to penicillin and azithromycin in 11/16 and 12/16 cases, respectively. Almost all cases of resistance emergence converged on mutations within ribosomal components or the mtrRCDE efflux pump for azithromycin-based selection and mtrRCDE, penA, and rpoB for penicillin selection, thus supporting constrained adaptive solutions despite divergent evolutionary starting points across the genus for these particular drugs. Though drug-selected loci were limited, we do identify novel resistance-imparting mutations. Continuing to explore paths to resistance across different experimental conditions and genomic backgrounds, which could shunt evolution down alternative evolutionary trajectories, will ultimately flesh out the full Neisseria resistome.IMPORTANCENeisseria gonorrhoeae is a global threat to public health due to its rapid acquisition of antibiotic resistance to all first-line treatments. Recent work has documented that alleles acquired from close commensal relatives have played a large role in the emergence of resistance to macrolides and beta-lactams within gonococcal populations. However, commensals have been relatively underexplored for the resistance genotypes they may harbor. This leaves a gap in our understanding of resistance that could be rapidly acquired by the gonococcus through a known highway of horizontal gene exchange. Here, we characterize resistance mechanisms that can emerge in commensal Neisseria populations via in vitro selection to multiple antimicrobials and begin to define the number of paths to resistance. This study, and other similar works, may ultimately aid both surveillance efforts and clinical diagnostic development by nominating novel and conserved resistance mechanisms that may be at risk of rapid dissemination to pathogen populations.

Rolling the evolutionary dice: Neisseria commensals as proxies for elucidating the underpinnings of antibiotic resistance mechanisms and evolution in human pathogens.

Species within the genus Neisseria are especially adept at sharing adaptive allelic variation across species' boundaries, with commensal species repeatedly transferring resistance to their pathogenic relative N. gonorrhoeae. However, resistance in commensal Neisseria is infrequently characterized at both the phenotypic and genotypic levels, limiting our ability to predict novel and potentially transferable resistance mechanisms that ultimately may become important clinically. Unique evolutionary starting places of each Neisseria species will have distinct genomic backgrounds, which may ultimately control the fate of evolving populations in response to selection, as epistatic and additive interactions may coerce lineages along divergent evolutionary trajectories. However alternatively, similar genetic content present across species due to shared ancestry may constrain the adaptive solutions that exist. Thus, identifying the paths to resistance across commensals may aid in characterizing the Neisseria resistome - or the reservoir of alleles within the genus, as well as its depth. Here, we use in vitro evolution of four commensal species to investigate the potential for and repeatability of resistance evolution to two antimicrobials, the macrolide azithromycin and the ß-lactam penicillin. After 20 days of selection, commensals evolved elevated minimum inhibitory concentrations (MICs) to penicillin and azithromycin in 11/16 and 12/16 cases respectively. Almost all cases of resistance emergence converged on mutations within ribosomal components or the mtrRCDE efflux pump for azithromycin-based selection, and mtrRCDE or penA for penicillin selection; thus, supporting constrained adaptive solutions despite divergent evolutionary starting points across the genus for these particular drugs. However, continuing to explore the paths to resistance across different experimental conditions and genomic backgrounds, which could shunt evolution down alternative evolutionary trajectories, will ultimately flesh out the full Neisseria resistome.