Biology subjects

Babbitt, G. A.

Publications and source records attributed to Babbitt, G. A..

11 recordsLinked to original sources

bioRxiv2025

An ancient evolutionary calculus for attention signaling retained in modern music

What is in a song? Although music has long been subjected to philosophical and scientific study, the mechanism(s) of fitness or quality signaled through music has not. As music and dance functionally aim to hold an observers attention, we investigate the features of sound that stimulate attentive behavior in an audience. We propose a calculus of attention to characterize a singers f(x): Control of vocal performance, f(x); Energy within vocal performance, f'(x); and Surprise or change in direction of performance, f''(x) (CES). We develop theory and software to, (A) extract CES, (B) dynamically map trajectories within the ternary space of CES, and (C) measure song stability (i.e. quality) and individuality. We validate that both human music and animal vocalization maintain high stability in the space of CES, with professional musicians being most stable. CES measurement also reflects differences in expert vs. novice singing ability in both humans and canaries as well as constraints during live audience interaction as well. We conclude that quality signaling via this calculus of attention reflects a cognitive focus upon motion in the environment created by selective pressures of intra/interspecific conflicts formed after the Ediacaran-Cambrian boundary and still retained in music today.. Lay SummaryMost animals (including humans) pay attention to specific audiovisual stimuli within their environment. Here we explore common features of these stimuli and propose a single mathematical framework (with software) for detecting and measuring the quality of performance regarding attention signaling in the context of sound. We show that musical sounds are exceptionally attractive to our attention and that attention signaling in music probably has ancient evolutionary roots that connect to when early life forms started to evolve swift directed movement and the sensory capacities to detect it.

animal behavior and cognition↗

bioRxiv2025

Methionine matters: a common mechanism of viral inhibition of host defense identified via AI-assisted molecular dynamics

Diverse groups of viruses infecting higher eukaryotes inhibit mRNA export via physical blockage of the Rae1-Nup98 complex within the host cells nucleopore. This is thought to most often involve the critical placement of hydrophilic flanked single methionine residues along polypeptide extensions that reach into the nucleopore. However, it is unknown how this presumably conserved mechanism might function across diverse viral taxa. Here we employ a comparative molecular dynamics (MD) approach comparing motions of wild-type and mutant viral proteins in Rae-Nup98 bound vs. unbound states. Our comparisons of MD simulations are enhanced by kernel-based denoising allowing the isolation of non-random functional dynamics from random thermal noise. We demonstrate that despite large structural differences, three evolutionarily distinct viral systems (i.e. VSV M protein, SARS-CoV2 ORF 6, and KSHV ORF 10) share nearly identical single methionine-dependent functional dynamics related to the host cell inhibition of nuclear transport. This finding strongly supports a convergently-evolved common functional mechanism across viruses involving specific structural placement of non-polar residues like methionine and potentially providing a common therapeutic target for broad spectrum anti-viral treatment.

microbiology↗

bioRxiv2025

Musicality in protein interaction dynamics informs the multi-scale evolution of prosocial behavior

Like animal vocalization and display, human singing and dancing allows non-verbal establishment of behavioral co-relation (i.e. correlation) between individuals. The predictable mathematical structure of music is its most defining acoustic property, allowing human synchronization of both physical behavior and emotion. In the biomolecular world, some proteins also interact in groups to achieve strong spatiotemporal co-relationships. This is prominent in amyloids, where many disordered fibrils individually conform to overall solenoid structures. We hypothesize that the vibrational frequencies captured during amyloid protein interactions may also exhibit elements of musicality related to this form of prosociality. Here, we develop a non-abstract data sonification method for computer-simulated molecular dynamic interactions. We apply auto-correlational and spectral cross-correlational analyses to a collection of sounds, defining 11 acoustic features that allow accurate machine learning classification of music from other types of natural sounds. By analyzing statistical shifts in these correlative features defining musicality, we demonstrate that amyloid interactions are more speech-like and musical than less structurally conforming protein interactions, primarily due to significant shifts in memory (persistence) and first order autocorrelation. We also find that music has less feature shift away from animal vocalization than human speech, suggesting it may have predated the evolution of language.

animal behavior and cognition↗

bioRxiv2023

ATOMDANCE: interpretable kernel-based learning for denoising functional and evolutionary comparisons of protein dynamics

Comparative methods in molecular biology and molecular evolution rely exclusively upon the analysis of DNA sequence and protein structure, both static forms of information. However, it is widely accepted that protein function results from changes in dynamic machine-like motions induced by molecular interactions, a type of data for which comparative methods of analysis are challenged by the large fraction of protein motion created by random thermal noise in the surrounding solvent. Here, we introduce ATOMDANCE, a suite of statistical and kernel-based machine learning tools designed for comparing and denoising functional states of protein motion captured in time-series from molecular dynamics simulations. ATOMDANCE employs interpretable Gaussian kernel functions to compute site-wise maximum mean discrepancy (MMD) between learned features of motion representing two functional, malfunctional or evolutionary protein states (e.g. bound vs. unbound, wild-type vs. mutant). ATOMDANCE derives empirical p-values identifying functional similarity/difference in dynamics at each amino acid site on the protein. ATOMDANCE also employs MMD to contextually analyze potential random amino-acid replacements thus allowing for a site-wise test of neutral vs. non-neutral evolution in the divergence of dynamic function in protein homologs. Lastly, ATOMDANCE also employs mixed-model ANOVA combined with graph network community detection to identify functional shifts in protein regions that exhibit time-coordinated dynamics or resonance of motion across sites. Here, we demonstrate the utility of the software for identifying key sites involved in dynamic responses during functional binding interactions involving DNA, small molecule drugs, and virus-host recognition. We also demonstrate its utility in understanding dynamic resonance changes occurring during the allosteric activation of a pathogenic protease. ATOMDANCE offers a user-friendly interface and only requires an input structure, topology and trajectory files for each of the two proteins being compared (i.e .pdb, .prmtop, and .nc). A separate interface for generating molecular dynamics simulations via open-source tools is also offered. Lay Audience Summary - ATOMDANCE is a suite of software pipelines controlled by a single user interface and designed to comprehensively simulate, calculate and compare protein motions between two functional or evolutionary states while controlling for random noise. It is useful for finding amino acid sites on a given protein that are important in binding other proteins, DNA, or drugs/toxins. It can also be used to assess the effect of genetic mutation on protein motion, and identifies regions of sites on proteins that tend to move together or resonate as a whole unit or community.

biophysics↗

bioRxiv2022

Evolution of drug resistance drives progressive destabilizations in functionally conserved molecular dynamics of the flap region of the HIV-1 protease

The HIV-1 protease is one of several common key targets of combination drug therapies for human immunodeficiency virus infection and acquired immunodeficiency syndrome (HIV/AIDS). During the progression of the disease, some individual patients acquire -drug resistance due to mutational hotspots on the viral proteins targeted by combination drug therapies. It has recently been discovered that drug-resistant mutations accumulate on the flap region of the HIV-1 protease, which is a critical dynamic region involved in non-specific polypeptide binding during invasion and infection of the host cell. In this study, we utilize machine learning assisted comparative molecular dynamics, conducted at single amino acid site resolution, to investigate the dynamic changes that occur during functional dimerization and polypeptide binding of the main protease. We use a multi-agent machine learning model to identify conserved dynamics of the HIV-1 main protease that are preserved across simian and feline protease orthologs (SIV and FIV). We also investigate changes in dynamics due to common drug-resistant mutations in many patients. We find that a key functional site in the flap region, a solvent-exposed isoleucine (ILE50) and surrounding sites that control flap dynamics is often targeted by drug-resistance mutations, likely leading to malfunctional molecular dynamics affecting the overall flexibility of the flap region. We conclude that better long term patient outcomes may be achieved by designing drugs that target protease regions which are less dependent upon single sites with large functional binding effects.

bioinformatics↗

bioRxiv2022

Persistent cross-species SARS-CoV-2 variant infectivity predicted via comparative molecular dynamics simulation

Widespread human transmission of SARS-CoV-2 highlights the substantial public health, economic, and societal consequences of virus spillover from wildlife and also presents a repeated risk of reverse spillovers back to naive wildlife populations. We employ comparative statistical analyses of a large set of short-term molecular dynamic (MD) simulations to investigate potential human to bat (Rhinolophus macrotis) cross-species infectivity allowed by the binding of SARS-CoV-2 receptor-binding domain (RBD) to angiotensin-converting enzyme 2 (ACE2) across the bat progenitor strain and emerging human strain variants of concern (VOC). We statistically compare the dampening of atom motion during binding across protein sites upon the formation of the RBD/ACE2 binding interface using bat vs. human target receptors (i.e. bACE2 and hACE2). We report that while the bat progenitor viral strain RaTG13 shows some pre-adaption to binding hACE2, it also exhibits stronger overall affinity to bACE2. However, while the early emergent human strains and later VOCs exhibit robust binding to both hACE2 and bACE2, the delta and omicron variants exhibit evolutionary adaption of binding to hACE2. However, we conclude there is a still significant risk of mammalian cross-species infectivity of human VOCs during upcoming waves of infection as COVID-19 transitions from a pandemic to endemic status.

bioinformatics↗

bioRxiv2021

Identifying protein sites contributing to vaccine escape via statistical comparisons of short-term molecular dynamics simulations

The identification of viral mutations that confer escape from antibodies is crucial for understanding the interplay between immunity and viral evolution. We describe a molecular dynamics (MD) based approach that scales well to a desktop computer with a high-end modern graphics processor and enables the user to identify protein sites that are prone to vaccine escape in a viral antigen. We first implement our MD pipeline to employ site-wise calculation of Kullback-Leibler divergence in atom fluctuation over replicate sets of short-term MD production runs thus enabling a statistical comparison of the rapid motion of influenza hemagglutinin (HA) in both the presence and absence of three well-known neutralizing antibodies. Using this simple comparative method applied to motions of viral proteins, we successfully identified in silico all previously empirically confirmed sites of escape in influenza HA, predetermined via selection experiments and neutralization assays. Upon the validation of our computational approach, we then surveyed potential hot spot residues in the receptor binding domain of the SARS-CoV-2 virus in the presence of COVOX-222 and S2H97 antibodies. We identified many single sites in the antigen-antibody interface that are similarly prone to potential antibody escape and that match many of the known sites of mutations arising in the SARS-CoV-2 variants of concern. In the omicron variant, we find only minimal adaptive evolutionary shifts in the functional binding profiles of both antibodies. In summary, we provide a fast and accurate computational method to monitor hot spots of functional evolution in antibody binding footprints.

bioinformatics↗

bioRxiv2020

A comparative survey of betacoronavirus strain molecular dynamics identifies key ACE2 binding sites

Comparative functional analysis of the dynamic interactions between various Betacoronavirus mutant strains and broadly utilized target proteins such as ACE2 and CD26, is crucial for a more complete understanding of zoonotic spillovers of viruses that cause diseases such as COVID-19. Here, we employ machine learning to replicated sets of nanosecond scale GPU accelerated molecular dynamics simulations to statistically compare and classify atom motions of these target proteins in both the presence and absence of different endemic and emergent strains of the viral receptor binding domain (RBD) of the S spike glycoprotein. Machine learning was used to identify functional binding dynamics that are evolutionarily conserved from bat CoV-HKU4 to human endemic/emergent strains. Conserved dynamics regions of ACE2 involve both the N-terminal helices, as well as a region of more transient dynamics encompassing K353, Q325 and a novel motif AAQPFLL 386-92 that appears to coordinate their dynamic interactions with the viral RBD at N501. We also demonstrate that the functional evolution of Betacoronavirus zoonotic spillovers involving ACE2 interaction dynamics are likely pre-adapted from two precise and stable binding sites involving the viral bat progenitor strains interaction with CD26 at SAMLI 291-5 and SS 333-334. Our analyses further indicate that the human endemic strains hCoV-HKU1 and hCoV-OC43 have evolved more stable N-terminal helix interactions through enhancement of an interfacing loop region on the viral RBD, whereas the highly transmissible SARS-CoV-2 variants (B.1.1.7, B.1.351 and P.1) have evolved more stable viral binding via more focused interactions between the viral N501 and ACE2 K353 alone.

biophysics↗

bioRxiv2020

Information theoretics for the machine learning detection of functionally conserved and coordinated protein motions

Traditional information theoretic analysis of functionally conserved binding interactions described by multiple sequence alignments are unable to provide direct insights into the underlying strength, spatial distribution, and coordination of the biophysical motions that govern protein binding interactions during signaling and regulatory function. However, molecular dynamic (MD) simulations of proteins in bound vs. unbound conformational states can allow for the combined application of machine learning classification and information theory towards many problems posed by comparative protein dynamics. After both bound and unbound protein dynamic states are adequately sampled in MD software, they can be employed as a comparative training set for a binary classifier capable of discerning the complex dynamical consequences of protein binding interactions with DNA or other proteins. The statistical validation of the learner on MD simulations of homologs can be used to assess its ability to recognize functional protein motions that are conserved over evolutionary time scales. Regions of proteins with functionally conserved dynamics are identifiable by their ability to induce significant correlations in local learning performance across homologous MD simulations. Through case studies of Rbp subunit 4/7 interaction in RNA Pol II and DNA-protein interactions of TATA binding protein, we demonstrate this method of detecting functionally conserved protein dynamics. We also demonstrate how the concepts of relative entropy (i.e. information gain) and mutual information applied to the binary classification states of MD simulations can be used to compare the impacts of molecular variation on conserved dynamics and to identify coordinated motions involved in dynamic interactions across sites.

biophysics↗

bioRxiv2020

Function and evolution of B-Raf loop dynamics relevant to cancer recurrence under drug inhibition

Oncogenic mutations in the kinase domain of the B-Raf protein have long been associated with cancers involving the MAPK pathway. One constitutive MAPK activating mutation in B-Raf, the V600E (valine to glutamate) replacement occurring adjacent to a site of threonine phosphorylation (T599) occurs in many types of cancer, and in a large percentage of certain cancers, such as melanoma. Because ATP binding activity and the V600E mutation are both known to alter the physical behavior of the activation loop in the B-Raf ATP binding domain, this system is especially amenable to comparative analyses of molecular dynamics simulations modeling various genetic and drug class variants. Here, we employ machine learning enabled identification of functionally conserved protein dynamics to compare how the binding interactions of four B-Raf inhibitors impact the functional loop dynamics controlling ATP activation. We demonstrate that drug development targeting B-Raf has progressively moved towards ATP competitive inhibitors that demonstrate less tendency to mimic the functionally conserved dynamic changes associated with ATP activation and leading to the side effect of hyperactivation (i.e. inducing MAPK activation in non-tumorous cells in the absence of secondary mutation). We compare the functional dynamic impacts of V600E and other sensitizing and drug resistance causing mutations in the regulatory loops of B-Raf, confirming sites of low mutational tolerance in these regions. Lastly, we investigate V600E sensitivity of B-Raf loop dynamics in an evolutionary context, demonstrating that while sensitivity has an ancient origin with primitive eukaryotes, it was also secondarily increased during early jawed vertebrate evolution.

biophysics↗

bioRxiv2019

Machine learning based detection of genetic and drug class variant impact on functionally conserved protein binding dynamics

The application of statistical methods to comparatively framed questions about protein dynamics can potentially enable investigations of biomolecular function beyond the current sequence and structural methods in bioinformatics. However, chaotic behavior in single protein trajectories requires statistical inference be obtained from large ensembles of molecular dynamic (MD) simulations representing the comparative functional states of a given protein. Meaningful interpretation of such a complex form of big data poses serious challenges to users of MD. Here, we announce DROIDS v3.0, a molecular dynamic (MD) method + software package for comparative protein dynamics, incorporating many new features including maxDemon v1.0, a multi-method machine learning application that trains on large ensemble comparisons of concerted protein motions in opposing functional states and deploys learned classifications of these states onto newly generated protein dynamic simulations. Local canonical correlations in learning patterns generated from self-similar MD runs are used to identify regions of functionally conserved protein dynamics. Subsequent impacts of genetic and drug class variants on conserved dynamics can also be analyzed by deploying the classifiers on variant MD runs and quantifying how often these altered protein systems display the opposing functional states. Here, we present several case studies of complex changes in functional protein dynamics caused by temperature, genetic mutation, and binding interaction with nucleic acids and small molecules. We studied the impact of genetic variation on functionally conserved protein dynamics in ubiquitin and TATA binding protein and demonstrate that our learning algorithm can properly identify regions of conserved dynamics. We also report impacts to dynamics that correspond well with predicted disruptive effects of a variety of genetic mutations. In addition, we studied the impact of drug class variation on the ATP binding region of Hsp90, similarly identifying conserved dynamics and impacts that rank accordingly with how closely various Hsp90 inhibitors mimic natural ATP binding.\n\nStatement of significanceWe propose a statistical method as well as offer a user-friendly graphical interfaced software pipeline for comparing simulations of the complex motions (i.e. dynamics) of proteins in different functional states. We also provide both method and software to apply artificial intelligence (i.e. machine learning methods) that enable the computer to recognize complex functional differences in protein dynamics on new simulations and report them to the user. This method can identify dynamics important for protein function, as well as to quantify how the motions of molecular variants differ from these important functional dynamic states. For the first time, this method of analysis allows the impacts of different genetic backgrounds or drug classes to be examined within the context of functional motions of the specific protein system under investigation.

biophysics↗