arXiv subjects

John M. McBride

Publications and source records attributed to John M. McBride.

7 recordsLinked to original sources

arXiv2026

Population Ecology of Tunes

How cultural repertoires maintain diversity under selection is a fundamental question in cultural evolution. We address this using thirteen years of weekly popularity data for approximately 20,000 Irish traditional tunes, fitting ecological birth-process models under neutral, frequency-dependent, and per-tune selection hypotheses. We find strong evidence that tunes differ in intrinsic fitness - some are systematically more likely to be learned than others. We find that 29% of the variance in fitness can be explained by a mixture of social and melodic features. Some tunes appear to be carried along via linkage due to the tradition of playing tunes in sets, analogous to selective sweeps in genetics. By measuring changes in fitness over time and comparing this with recordings we precisely identify the mechanism by which a long-dormant tune can become fit through a popular recording. Despite the directional selection, repertoire diversity increases, driven by the continual arrival of new compositions. These results demonstrate that selection and diversity can coexist in a cultural ecosystem, and establish Irish traditional music as a quantitatively tractable system for studying the evolution of cultural variants and understanding what makes a tune stand out.

q-bio.PE↗

arXiv2026

Geometric-Chemical Distance Between Protein Surfaces

Proteins recognize, bind, and catalyze through molecular surfaces, where geometry and chemical patterning determine interaction. Comparing these surfaces requires both a geometric--chemical distance and a correspondence that relates one complete surface to another. Here we introduce IFACE (Intrinsic Field--Aligned Coupled Embedding). IFACE derives a symmetric geometric--chemical distance by optimizing a probabilistic coupling over intrinsic geometry, mean curvature, electrostatics, hydrophobicity, and hydrogen-bond propensity. The same coupling provides an explicit surface map. For molecular-dynamics conformers, IFACE distinguishes the same protein from distinct proteins more accurately than TM-distance and a Laplace--Beltrami spectral distance. A Jensen--Shannon distribution distance performs best in this binary identity test, because aggregate surface-feature distributions already identify each protein. A distance must also satisfy a global requirement: its pairwise values must place many distinct protein surfaces consistently in one space. We therefore tested IFACE across six protein families. It produces the strongest family classification and clustering among the distributional, spectral, MaSIF, and SurfaceID comparisons. The inferred maps preserve geodesic neighborhoods and transfer heme-centered pocket regions across cytochrome P450 proteins. IFACE therefore provides, from one construction, both a distance between complete protein surfaces and the local map that explains that distance.

q-bio.BM↗

arXiv2025

Musical consonance: a review of theory and evidence on perception and preference of auditory roughness in humans and other animals

The origins of consonance in human music has long been contested, and today there are three primary hypotheses: aversion to roughness, preference for harmonicity, and learned preferences from cultural exposure. While the evidence is currently insufficient to disentangle the contributions of these hypotheses, I propose several reasons why roughness is an especially promising area for future study. The aim of this review is to summarize and critically evaluate roughness theory and models, experimental data, to highlight areas that deserve further research. I identify 2 key areas: There are fundamental issues with the definition and interpretation of results due to tautology in the definition of roughness, and the lack of independence in empirical measurements. Despite extensive model development, there are many duplications and models have issues with data quality and overfitting. Future theory development should aim for model simplicity, and extra assumptions, features and parameters should be evaluated systematically. Model evaluation should aim to maximise the breadth of stimuli that are predicted.

physics.soc-ph↗

arXiv2024

Statistical Survey of Chemical and Geometric Patterns on Protein Surfaces as a Blueprint for Protein-mimicking Nanoparticles

Despite recent breakthroughs in understanding how protein sequence relates to structure and function, considerably less attention has been paid to the general features of protein surfaces beyond those regions involved in binding and catalysis. This paper provides a systematic survey of the universe of protein surfaces and quantifies the sizes, shapes, and curvatures of the positively/negatively charged and hydrophobic/hydrophilic surface patches as well as correlations between such patches. It then compares these statistics with the metrics characterizing nanoparticles functionalized with ligands terminated with positively and negatively charged ligands. These particles are of particular interest because they are also surface-patchy and have been shown to exhibit both antibiotic and anticancer activities - via selective interactions against various cellular structures - prompting loose analogies to proteins. Our analyses support such analogies in several respects (e.g., patterns of charged protrusions and hydrophobic niches similar to those observed in proteins), although there are also significant differences. Looking forward, this work provides a blueprint for the rational design of synthetic nanoobjects with further enhanced mimicry of proteins' surface properties.

q-bio.BM↗

arXiv2023

The Physical Logic of Protein Machines

Proteins are intricate molecular machines whose complexity arises from the heterogeneity of the amino acid building blocks and their dynamic network of many-body interactions. These nanomachines gain function when put in the context of a whole organism through interaction with other inhabitants of the biological realm. And this functionality shapes their evolutionary histories through intertwined paths of selection and adaptation. Recent advances in machine learning have solved the decades-old problem of how protein sequence determines their structure. However, the ultimate question regarding the basic logic of protein machines remains open: How does the collective physics of proteins lead to their functionality? and how does a sequence encode the full range of dynamics and chemical interactions that facilitate function? Here, we explore these questions within a physical approach that treats proteins as mechano-chemical machines, which are adapted to function via concerted evolution of structure, motion, and chemical interactions.

q-bio.BM↗

arXiv2022

AlphaFold2 can predict single-mutation effects

AlphaFold2 (AF) is a promising tool, but is it accurate enough to predict single mutation effects? Here, we report that the localized structural deformation between protein pairs differing by only 1-3 mutations -- as measured by the effective strain -- is correlated across \num{3901} experimental and AF-predicted structures. Furthermore, analysis of ${\sim} 11000$ proteins shows that the local structural change correlates with various phenotypic changes. These findings suggest that AF can predict the range and magnitude of single-mutation effects on average, and we propose a method to improve precision of AF predictions and to indicate when predictions are unreliable.

q-bio.BM↗

arXiv2019

Cross-cultural data shows musical scales evolved to maximise imperfect fifths

Musical scales are used throughout the world, but the question of how they evolved remains open. Some suggest that scales based on the harmonic series are inherently pleasant, while others propose that scales are chosen that are easy to communicate. However, testing these theories has been hindered by the sparseness of empirical evidence. Here, we assimilate data from diverse ethnomusicological sources into a cross-cultural database of scales. We generate populations of scales based on multiple theories and assess their similarity to empirical distributions from the database. Most scales tend to include intervals which are close in size to perfect fifths (``imperfect fifths''), and packing arguments explain the salient features of the distributions. Scales are also preferred if their intervals are compressible, which may facilitate efficient communication and memory of melodies. While scales appear to evolve according to various selection pressures, the simplest h imperfect-fifths packing model best fits the empirical data.

cs.SD↗