Biology subjects

Feehan, R.

Publications and source records attributed to Feehan, R..

6 recordsLinked to original sources

bioRxiv2026

Counting strands in outer membrane beta-barrels

Beta-barrel structures are critical components of bacterial outer membranes, where they facilitate transport, cell signaling, antibiotic resistance, and structural integrity. A key feature of beta-barrels is their strand count, which influences pore diameter, binding site locations, and functional properties. However, because of breaks in strands and the presence of strands in periplasmic domains and plug domains, manual counting is inefficient and current algorithms do not accurately determine barrel strand count. To address this, we refined our previous beta-barrel structural assessment tool, PolarBearal, to improve strand number identification in large-scale datasets. To enhance the accuracy of barrel strand number labeling, our updated algorithm integrates three structural criteria, namely inter-residue vector angles, hydrogen-bonding distances, and strand connectivity. Using this algorithm, we labeled strand numbers for 571,760 predicted outer membrane beta-barrel structures obtained from the AlphaFold2 database. Our algorithm has 97% accuracy in strand number assignments, and the resulting dataset facilitates assessment of the homogeneity of strand counts for different types of outer membrane proteins. The strand labeling also provides insights on beta-barrel strand distribution and evolutionary patterns, supporting further research in protein structure prediction and design. SignificanceThis work contributes an accurate, automated method for counting beta-barrel strands in bacterial outer membrane proteins. Methodological ImpactThe algorithm achieves 97% accuracy in strand counting, solving a technical problem that has hindered large-scale structural analysis. Previous manual methods were inefficient, and existing algorithms failed to handle the structural complexities of real beta-barrel proteins, including strand breaks and additional domains. Scale of ContributionBy annotating over 571,000 predicted structures from the AlphaFold2 database, this work represents the largest systematic characterization of beta-barrel strand distributions to date. This labeled dataset provides a resource for the structural biology community. Broader ApplicationsThis tool enables researchers understand structure-function relationships in outer membrane proteins, supporting advances in protein design, drug development targeting bacterial membranes.

bioinformatics↗

bioRxiv2023

MAHOMES II: A webserver for predicting if a metal binding site is enzymatic

Recent advances have enabled high-quality computationally generated structures for proteins with no solved crystal structures. However, protein function data remains largely limited to experimental methods and homology mapping. Since structure determines function, it is natural that methods capable of using computationally generated structures for functional annotations need to be advanced. Our laboratory recently developed a method to distinguish between metalloenzyme and non-enzyme sites. Here we report improvements to this method by upgrading our physicochemical features to alleviate the need for structures with sub-angstrom precision and using machine learning to reduce training data labeling error. Our improved classifier identifies protein bound metal sites as enzymatic or non-enzymatic with 94% precision and 92% recall. We demonstrate that both adjustments increased predictive performance and reliability on sites with sub-angstrom variations. We constructed a set of predicted metalloprotein structures with no solved crystal structures and no detectable homology to our training data. Our model had an accuracy of 90 - 97.5% depending on the quality of the predicted structures included in our test. Finally, we found the physicochemical trends that drove this models successful performance were local protein density, second shell ionizable residue burial, and the pockets accessibility to the site. We anticipate that our models ability to correctly identify catalytic metal sites could enable identification of new enzymatic mechanisms and improve de novo metalloenzyme design success rates. Significance statementIdentification of enzyme active sites on proteins with unsolved crystallographic structures can accelerate discovery of novel biochemical reactions, which can impact healthcare, industrial processes, and environmental remediation. Our lab has developed an ML tool for predicting sites on computationally generated protein structures as enzymatic and non-enzymatic. We have made our tool available on a webserver, allowing the scientific community to rapidly search previously unknown protein function space.

bioinformatics↗

bioRxiv2021

Machine Learning Differentiates Enzymatic and Non-Enzymatic Metals in Proteins

Metalloenzymes are 40% of all enzymes and can perform all seven classes of enzyme reactions. Because of the physicochemical similarities between the active sites of metalloenzymes and inactive metal binding sites, it is challenging to differentiate between them. Yet distinguishing these two classes is critical for the identification of both native and designed enzymes. Because of similarities between these two types of metal binding sites, finding physicochemical features that distinguish active and inactive metal sites can indicate aspects that are critical to enzyme function. In this work, we develop the largest structural dataset of enzymatic and non-enzymatic metalloprotein sites to date. We then use a decision-tree ensemble machine learning model to classify metals bound to proteins as enzymatic or non-enzymatic with 92.2% precision and 90.1% recall. Our model scores electrostatic and pocket lining features as more important than pocket volume, despite the fact that volume is the most quantitatively different feature between enzyme and non-enzymatic sites. Finally, we find our model has overall better performance in a side-to-side comparison against other methods that differentiate enzymatic from non-enzymatic sequences. We anticipate that our models ability to correctly identify which metal sites are responsible for enzymatic activity could enable identification of new enzymatic mechanisms and de novo enzyme design.

biophysics↗

bioRxiv2021

Membrane barrels are taller, fatter, inside-out soluble barrels

Up-and-down {beta}-barrel topology exists in both the membrane and soluble environment. However, {beta}-barrels are virtually the only topology that exist in the outer membrane. By comparing features of these structurally similar proteins, we can determine what features are particular to the environment rather than the fold. Here we compare structures of membrane {beta}-barrels to soluble {beta}-barrels and evaluate their relative size, shape, amino acid composition, hydrophobicity, and periodicity. We find that membrane {beta}-barrels are generally larger than soluble {beta}-barrels in with more strands per barrel and more amino acids per strand, making them wider and taller. We also find that membrane {beta}-barrels are inside-out soluble {beta}-barrels. The inward region of membrane {beta}-barrels have similar hydrophobicity to the outward region of soluble {beta}-barrels, and the outward region of membrane {beta}-barrels has similar hydrophobicity to the inward region of the soluble {beta}-barrels. Moreover, even though both types of {beta}-barrel have been assumed to have strands with amino acids that alternate in direction and hydrophobicity, we find that the membrane {beta}-barrels have more regular alternation than soluble {beta}-barrels. These features give insight into how membrane barrels maintain their fold and function in the membrane.

biophysics↗

bioRxiv2018

Evolution of Environmentally-Enforced, Repeat Protein Topology in the Outer Membrane

Outer membrane beta barrels (OMBBs) are the proteins on the surface of Gram negative bacteria. These proteins have diverse functions but only a single topology, the beta barrel. It has been suggested that this common fold is a repeat protein with the repeating unit of a beta hairpin. By grouping structurally solved OMBBs by sequence, a detailed evolutionary story unfolds. A strand-number based pathway manifests with progression from a primordial 8-stranded barrel to 16-stranded and then to 18-stranded barrels. The transitions from 16- to 18-stranded barrels show mechanisms of strand number variation without domain duplication, such as a loop to hairpin transition. This indicates that repeat protein topology can be perpetuated without genetic duplication likely because the topology is being enforced by the membrane environment. Moreover, we find the evolutionary trace is particularly prominent in the C-terminal half of OMBBs which may be relevant to understanding OMBB folding pathways.

biophysics↗

bioRxiv2018

Efflux Pumps Represent Possible Evolutionary Convergence onto the Beta Barrel Fold

There are around 100 types of integral outer membrane proteins in each Gram negative bacteria. All of these proteins have the same fold--an up-down {beta}-barrel. It has been suggested that all membrane {beta}-barrels other than lysins are homologous. Here we suggest that {beta}-barrels of efflux pumps have converged on this fold as well. By grouping structurally-solved outer membrane {beta}-barrels (OMBBs) by sequence we find evidence that the membrane environment may have led to convergent evolution of the barrel fold. Specifically, the lack of sequence linkage to other barrels coupled with distinctive structural differences, such as differences in strand tilt and barrel radius, suggest that efflux pumps have evolutionarily converged on the barrel. Finally, we find a possible ancestor for the OMBB efflux pumps as they are related to periplasmic components of the same pumps.

biophysics↗