Biology subjects

Rath, S. S.

Publications and source records attributed to Rath, S. S..

5 recordsLinked to original sources

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↗

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↗

bioRxiv2020

Hilbert space filling curves for interpretable point mutation effects on peptide conformational dynamics

Spontaneous self-organization of solid-binding peptides on single-layer atomic materials offers enormous potential in employing these systems for practical technological and medical applications. Molecular self-organization of peptides depends highly on their sequences which, in turn, affect their conformational behavior under aqueous conditions. Traditional ways of computationally studying the effect of mutations on the conformation states involves dimension reduction on cosine and sine transformed torsion angles, often represented as Ramachandran plots. Although these studies successfully cluster conformation states, they fail to intuitively characterize the effect of the point mutation(s) directly, necessitating further data analysis. Here, we apply Hilbert Space-Filling-Curve (HSFC) on the torsion angles and demonstrate intuitive visualization for the effect of point mutations on conformation states and secondary structure dynamics along a reaction coordinate. We perform molecular dynamics (MD) simulation on wild-type graphene binding peptide (WT-GrBP5). The 12-amino acid long peptide was selected by directed evolution and known to self-organize on atomically flat surface of graphene only under low-neutral pH at room temperature. A charge neutral mutant, M9-GrBP5, on the other hand, assembles at a broader range of pHs at room temperature, as expected. The HSFC shows clearly that the mutated amino acids in M9 do not correlate with the reaction coordinate of pH change, unlike that of WT, confirming heuristic knowledge. Understanding the effect of specific amino acid {varphi}-{psi} pairs that contribute most to the changes in the conformational space of the peptide with changing conditions, will help in analyzing effects of point mutations in peptide sequences. The knowledge of the conformational behavior of solid binding peptides, in general, and its effect on their self-organization propensities on solid surfaces would lead to the rational design of sequences that form soft bio/nano hybrid interfaces in the future towards robust strategies for surface biofunctionalization, in general, and bioelectronics and biosensors, in particular.

biophysics↗

bioRxiv2019

A Generalized Similarity Metric for Peptide Binding Affinity Prediction

The ability to capture the relationship between similarity and functionality would enable the predictive design of peptide sequences for a wide range of implementations from developing new drugs to molecular scaffolds in tissue engineering and biomolecular building blocks in nanobiotechnology. Similarity matrices are widely used for detecting sequence homology but depend on the assumption that amino acid mutational frequencies reflected by each matrix are relevant to the system in which they are applied. Increasingly, neural networks and other statistical learning models solve problems related to functional prediction but avoid using known features to circumvent unconscious bias. We demonstrated an iterative alignment method that enhances predictive power of similarity matrices based on a similarity metric, the Total Similarity Score. A generalized method is provided for application to amino acid sequences from inorganic and organic systems by benchmarking it on the debut quartz-binder set and 3 peptide-protein sets from the Immune Epitope Database. Pearson and Spearman Rank Correlations show that by treating the gapless Total Similarity Score as a predictor of relative binding affinity, prediction of test data has a 0.5-0.7 Pearson and Spearman Rank correlation. with respect to size of the dataset. Since the benchmarks used herein are from a solid-binding peptide and a protein-peptide system, our proposed method could prove to be a highly effective general approach for establishing the predictive sequence-function relationships of among the peptides with different sequences and lengths in a wide range of biotechnology, nanomedicine and bioinformatics applications.\n\nAuthor SummaryThe significance of this work is to expand the applicability of a known metric for describing the function of tiny proteins also called peptides. The Total Similarity Score (TSS) can describe how similar a peptide, or a group of peptides are to another group of sequences with a known or suspected function. A peptide/group of peptides will always have a high TSS if it contains the same or similar amino acids in the same positions. This metric can therefore be used to select peptides for useful functions based purely on conserved amino acids in unknown positions. The greedy search algorithm used to learn how similar amino acids are to each other has been shown to be marginally effective in this larger dataset. Therefore, we argue that the TSS metric is a highly useful one for predicting peptide affinity but a different machine learning algorithm should be applied to make full use of it.

bioinformatics↗

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↗