Biology subjects

Nakano-Baker, O.

Publications and source records attributed to Nakano-Baker, O..

5 recordsLinked to original sources

bioRxiv2026

Contrastive Learning Improves Generalization on M2OR Olfactory Binding Dataset

Olfaction-based biosensors stand to have profound impacts in fields such as medical diagnostics, national security, and drug discovery. A promising mode of vapor phase biodetection mimics volatile organic compound-olfactory receptor (VOC-OR) interactions with target-specific protein or peptide probes; however, lack of experimental coverage remains a core bottleneck. Current computational approaches aim to improve prediction of VOC-OR binding to reduce experimental burden, and current machine learning approaches fail to demonstrate a level of generalization that addresses current engineering challenges. Here, using the Molecular to Olfactory Dataset (M2OR) and Insect Olfactory Binding Protein Database (iOBPdb) datasets, data partitioning methods and their performance are evaluated on a neural network and contrastive learning framework on such VOC-OR binding datasets, varying: model training, architecture, and structure of the input data distribution. It was found that holding out certain chemical or proteomic motifs from the training data diminished model performance with a two layer neural network model. The model was able to generalize to state of the art performance (0.88 AUCROC) on every data split by applying a contrastive learning objective defined by shared molecular constituents and a stochastic sampler. This suggests the contrastive objective provides a strong inductive bias for neural networks to predict novel VOC-OR pairs. These findings highlight that model performance on pairwise biological interaction datasets can be inflated through identity leakage from constituent overlap, and emphasize the need for more robust evaluation protocols. Furthermore, we show contrastive regularization mitigates dataset degeneracy through enforcement of compatibility in the models latent geometry that may extend to other binder target-datasets where identity leakage confounds evaluation.

bioinformatics↗

bioRxiv2022

Data-Driven Design of Protein-Derived Peptide Multiplexes for Biomimetic Detection of Exhaled Breath VOC Profiles

Exhaled human breath contains a rich mixture of volatile organic compounds (VOCs) whose concentration can vary in response to disease or other stressors. Using simulated odorant-binding proteins (OBPs) and machine learning methods, we designed a multiplex of short VOC- and carbon-binding peptide probes that detect the characteristic "VOC fingerprint". Specifically, we target VOCs associated with COVID-19 in a compact, molecular sensor array that directly transduces vapor composition into multi-channel electrical signals. Rapidly synthesizable, chimeric VOC- and solid-binding peptides were derived from selected OBPs using multi-sequence alignment with protein database structures. Selective peptide binding to targeted VOCs and sensor surfaces was validated using surface plasmon resonance spectroscopy and quartz crystal microbalance. VOC sensing was demonstrated by peptide-sensitized, exposed-channel carbon nanotube transistors. The data-to-device pipeline enables the development of novel devices for non-invasive monitoring, diagnostics of diseases, and environmental exposures assessment.

bioengineering↗

bioRxiv2022

iOBPdb: A Database for Experimentally Determined Functional Characterization of Odorant Binding Proteins

Summary/AbstractOdorant binding proteins, OBPs, are a diverse family of small, globular, extra-cellular proteins solubilize volatile organic compounds (VOCs) so they can be internalized and transported by an organism. Since their initial discovery in the early eighties 1, thousands of OBPs have been identified through genome sequencing and characterized by fluorescence ligand binding assays 2. While individual OBPs have been studied in the context of their roles in specific organism, there have been no studies towards the understanding of the comparative structure-function relations of all known OBPs, primarily due to a lack of a centralized database that incorporates the binding affinity with the structure of all OBPs. Incorporating OBP information into a database requires not only an extensive search of all existing resources, but also creating a useful platform that relates sequence structures to target functions. Combining 215 functional studies containing 381 unique OBPs from 91 insect species we created a database, iOBPdb: https://iobpdb.herokuapp.com, of OBP binding affinities for a wide range of VOC targets. We demonstrate here that the construction of this initial database provides powerful search and associative capabilities including interrogating odor binding proteins as clusters and groups by sequence similarity versus protein and target molecular weights, and by the functional groups of the VOC targets. The comparative interrogation of the probe-target recognition allows for a more comprehensive understanding of the underlying structural features of all OBPs that had not been possible by only examining the OBPs individually. We present our results in a variety of phylogenetic representations as well as providing the binding profiles of OBP groups to VOC functional moieties. Potential applications include development of molecular probes for biosensors, novel bioassays and drugs, discovery of novel pesticides which inhibit VOC / OBP interactions, as well providing a foundational basis for the functional understanding of odor sensing and perception in the brain. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=158 SRC="FIGDIR/small/498339v2_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@9c2fa0org.highwire.dtl.DTLVardef@1fad38aorg.highwire.dtl.DTLVardef@56175forg.highwire.dtl.DTLVardef@189797_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2021

Deep Directed Evolution of Solid Binding Peptides for Quantitative Big-Data Generation

Proteins have evolved over millions of years to mediate and carry-out biological processes efficiently. Directed evolution approaches have been used to genetically engineer proteins with desirable functions such as catalysis, mineralization, and target-specific binding. Next-generation sequencing technology offers the capability to discover a massive combinatorial sequence space that is costly to sample experimentally through traditional approaches. Since the permutation space of protein sequence is virtually infinite, and evolution dynamics are poorly understood, experimental verifications have been limited. Recently, machine-learning approaches have been introduced to guide the evolution process that facilitates a deeper and denser search of the sequence-space. Despite these developments, however, frequently used high-fidelity models depend on massive amounts of properly labeled quality data, which so far has been largely lacking in the literature. Here, we provide a preliminary high-throughput peptide-selection protocol with functional scoring to enhance the quality of the data. Solid binding dodecapeptides have been selected against molybdenum disulfide substrate, a two-dimensional atomically thick semiconductor solid. The survival rate of the phage-clones, upon successively stringent washes, quantifies the binding affinity of the peptides onto the solid material. The method suggested here provides a fast generation of preliminary data-pool with [~]2 million unique peptides with 12 amino-acids per sequence by avoiding amplification. Our results demonstrate the importance of data-cleaning and proper conditioning of massive datasets in guiding experiments iteratively. The established extensive groundwork here provides unique opportunities to further iterate and modify the technique to suit a wide variety of needs and generate various peptide and protein datasets. Prospective statistical models developed on the datasets to efficiently explore the sequence-function space will guide towards the intelligent design of proteins and peptides through deep directed evolution. Technological applications of the future based on the peptide-single layer solid based bio/nano soft interfaces, such as biosensors, bioelectronics, and logic devices, is expected to benefit from the solid binding peptide dataset alone. Furthermore, protocols described herein will also benefit efforts in medical applications, such as vaccine development, that could significantly accelerate a global response to future pandemics.

molecular biology↗

bioRxiv2019

VSEPRnet: Physical structure encoding of sequence-based biomolecules for functionality prediction: Case study with peptides

Predicting structure-dependent functionalities of biomolecules is crucial for accelerating a wide variety of applications in drug-screening, biosensing, disease-diagnosis, and therapy. Although the commonly used structural \"fingerprints\" work for biomolecules in traditional informatics implementations, they remain impractical in a wide range of machine learning approaches where the model is restricted to make data-driven decisions. Although peptides, proteins, and oligonucleotides have sequence-related propensities, representing them as sequences of letters, e.g., in bioinformatics studies, causes a loss of most of their structure-related functionalities. Biomolecules lacking sequence, such as polysaccharides, lipids, and their peptide conjugates, cannot be screened with models using the letter-based fingerprints. Here we introduce a new fingerprint derived from valence shell electron pair repulsion structures for small peptides that enables construction of structural feature-maps for a given biomolecule, regardless of the sequence or conformation. The feature-map introduced here uses a simple encoding derived from the molecular graph - atoms, bonds, distances, bond angles, etc., that make up each of the amino acids in the sequence, allowing a Residual Neural network model to take greater advantage of information in molecular structure. We make use of the short peptides binding to Major-Histocompatibility-Class-I protein alleles that are encoded in terms of their extended structures to predict allele-specific binding-affinities of test-peptides. Predictions are consistent, without appreciable loss in accuracy between models for different length sequences, marking an improvement over the current models. Biological processes are heterogeneous interactions, which justifies encoding all biomolecules universally in terms of structures and relating them to their functionality. The capabilities facilitated by the model expands the paradigm in establishing structure-function correlations among small molecules, short and longer sequences including large biomolecules, and genetic conjugates that may include polypeptides, polynucleotides, RNAs, lipids, peptidoglycans, peptido-lipids, and other biomolecules that could be implemented in a wide range of medical and nanobiotechnological applications in the future.

bioinformatics↗