Biology subjects

McBride, J. M.

Publications and source records attributed to McBride, J. M..

8 recordsLinked to original sources

bioRxiv2026

Joint Geometric--Chemical Distance for 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 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.

bioinformatics↗

bioRxiv2024

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.

biochemistry↗

bioRxiv2024

Molecular characterization of response to etrolizumab and anti-TNF reveals treatment resistance in ulcerative colitis is associated with the abundance of residual neutrophil subsets and inflammatory fibroblast populations

Ulcerative colitis (UC) is a chronic inflammatory large bowel disease characterized by immune cell infiltration and continuous erosion of intestinal crypts, causing severe ulceration and abdominal pain. In the etrolizumab Phase 3 studies, transcriptional analyses of colonic biopsies revealed reductions in genes associated with aEb7+ intraepithelial lymphocytes with etrolizumab but not adalimumab. Both treatments significantly reduced stromal and myeloid cell-associated genes, with changes associated with MCS remission status. Generation of a single-cell atlas from inflamed and uninflamed colonic biopsies from UC patients led to the identification of thirty-six discrete cell populations, including cells of the myeloid compartment. The UC atlas was used to generate cell-specific signatures, allowing for cellular deconvolution of the Phase 3 datasets. It revealed significant reductions in neutrophil subsets, monocyte-derived macrophages, and inflammatory fibroblasts, as well as increases in colonic epithelial cells common to both etrolizumab and adalimumab. Pseudo-time trajectory analyses identified four unique neutrophil subsets with unique cell phenotypes reflecting changes in cell state or differentiation from PADI4hi, OSMhi, MX1hi, and ultimately to CXCR4hi populations. PADI4hi and OSMhi neutrophils exhibited high levels of proteases (MMP9, LYZ), inflammatory cytokines (CXCL1, IL1B, OSM), and abundant cytokine or chemokine receptors (CXCR1, CXCR2). MX1 populations expressed markers indicating prior IFN exposure (MX1, IFIT1). In contrast, more differentiated or mature neutrophils exhibited high levels of CXCL2, TNF-a, and CXCR4, as well as angiogenic factors like VEGFA. PADI4hi and OSMhi neutrophils, we predict, have abundant cytokine and chemokine interactions with inflammatory fibroblasts within the inflamed colon, such as OSM: OSMR and IL1B: IL1R1 interactions. Changes in PADI4hi and OSMhi neutrophils were closely associated with MCS remission in both etrolizumab and adalimumab-treated patients. In contrast, only minor changes in CXCR4hi neutrophils were observed and not associated with clinical outcomes. Our results suggest that neutrophils are not only heterogeneous in phenotype but have abundant cell-cell interactions in inflamed colonic tissue that are likely implicated in maintaining chronic disease activity. We hypothesize that limiting the interactions between neutrophils and other myeloid cells with resident cells such as inflammatory fibroblasts may reduce the production of inflammatory mediators and limit activation and infiltration of neutrophils, which may be necessary for achieving greater rates of clinical remission in response to interventional agents.

immunology↗

bioRxiv2023

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.

biophysics↗

bioRxiv2023

AlphaFold-predicted deformation probes changes in protein stability

AI algorithms have proven to be excellent predictors of protein structure, but whether and how much these algorithms can capture the underlying physics remains an open question. Here, we aim to test this question using the Alphafold2 (AF) algorithm: We use AF to predict the subtle structural deformation induced by single mutations, quantified by strain, and compare with experimental datasets of corresponding perturbations in folding free energy {Delta}{Delta}G. Unexpectedly, we find that physical strain alone - without any additional data or computation - correlates almost as well with {Delta}{Delta}G as state-of-the-art energy-based and machine-learning predictors. This indicates that the AF-predicted structures alone encode fine details about the energy landscape. In particular, the structures encode significant information on stability, enough to estimate (de-)stabilizing effects of mutations, thus paving the way for the development of novel, structure-based stability predictors for protein design and evolution.

biophysics↗

bioRxiv2023

Myeloid cell influx into the colonic epithelium is associated with disease severity and non-response to anti-Tumor Necrosis Factor Therapy in patients with Ulcerative Colitis.

Ulcerative colitis (UC) is an idiopathic chronic inflammatory disease of the colon with sharply rising global prevalence. Dysfunctional epithelial compartment (EC) dynamics are implicated in UC pathogenesis although EC-specific studies are sparse. Applying orthogonal high-dimensional EC profiling to a Primary Cohort (PC; n=222), we detail major epithelial and immune cell perturbations in active UC. Prominently, reduced frequencies of mature BEST4+OTOP2+ absorptive and BEST2+WFDC2+ secretory epithelial enterocytes were associated with the replacement of homeostatic, resident TRDC+KLRD1+HOPX+ {gamma}{delta}+ T cells with RORA+CCL20+S100A4+ TH17 cells and the influx of inflammatory myeloid cells. The EC transcriptome (exemplified by S100A8, HIF1A, TREM1, CXCR1) correlated with clinical, endoscopic, and histological severity of UC in an independent validation cohort (n=649). Furthermore, therapeutic relevance of the observed cellular and transcriptomic changes was investigated in 3 additional published UC cohorts (n=23, 48 and 204 respectively) to reveal that non-response to anti-Tumor Necrosis Factor (anti-TNF) therapy was associated with EC related myeloid cell perturbations. Altogether, these data provide high resolution mapping of the EC to facilitate therapeutic decision-making and personalization of therapy in patients with UC.

immunology↗

bioRxiv2022

A general theory of specific binding: insights from a genetic, mechano-chemical protein model

Proteins need to selectively interact with specific targets among a multitude of similar molecules in the cell. But despite a firm physical understanding of binding interactions, we lack a general theory of how proteins evolve high specificity. Here, we present such a model that combines chemistry, mechanics and genetics, and explains how their interplay governs the evolution of specific protein-ligand interactions. The model shows that there are many routes to achieving molecular discrimination - by varying degrees of flexibility and shape/chemistry complementarity - but the key ingredient is precision. Harder discrimination tasks require more collective and precise coaction of structure, forces and movements. Proteins can achieve this through correlated mutations extending far from a binding site, which fine-tune the localized interaction with the ligand. Thus, the solution of more complicated tasks is enabled by increasing the protein size, and proteins become more evolvable and robust when they are larger than the bare minimum required for discrimination. The model makes testable, specific predictions about the role of flexibility and shape mismatch in discrimination, and how evolution can independently tune affinity and specificity. Thus, the proposed theory of specific binding addresses the natural question of "why are proteins so big?". A possible answer is that molecular discrimination is often a hard task best performed by adding more layers to the protein.

biochemistry↗

bioRxiv2020

Structural asymmetry along protein sequences and co-translational folding

Proteins are translated from the N- to the C-terminal, raising the basic question of how this innate directionality affects their evolution. To explore this question, we analyze 16,200 structures from the protein data bank (PDB). We find remarkable enrichment of -helices at the C terminal and {beta}-sheets at the N terminal. Furthermore, this -{beta} asymmetry correlates with sequence length and contact order, both determinants of folding rate, hinting at possible links to co-translational folding (CTF). Hence, we propose the slowest-first scheme, whereby protein sequences evolved structural asymmetry to accelerate CTF: the slowest-folding elements (e.g. {beta}-sheets) are positioned near the N terminal so they have more time to fold during translation. Our model predicts that CTF can be accelerated, up to double the rate, when folding time is commensurate with translation time; analysis of the PDB reveals that structural asymmetry is indeed maximal in this regime. This correspondence is greater in prokaryotes, which generally require faster protein production. Altogether, this indicates that accelerating CTF is a substantial evolutionary force whose interplay with stability and functionality is encoded in sequence asymmetry.

biophysics↗