Computational assessment of the feasibility of protonation-based protein sequencing

Application of Machine Learning in the Quantitative Analysis of the Surface Characteristics of Highly Abundant Cytoplasmic Proteins: Toward AI-Based Biomimetics

Proteins in the crowded environment of human cells have often been studied regarding nonspecific interactions, misfolding, and aggregation, which may cause cellular malfunction and disease. Specifically, proteins with high abundance are more susceptible to these issues due to the law of mass action. Therefore, the surfaces of highly abundant cytoplasmic (HAC) proteins directly exposed to the environment can exhibit specific physicochemical, structural, and geometrical characteristics that reduce nonspecific interactions and adapt to the environment. However, the quantitative relationships between the overall surface descriptors still need clarification. Here, we used machine learning to identify HAC proteins using hydrophobicity, charge, roughness, secondary structures, and B-factor from the protein surfaces and quantified the contribution of each descriptor. First, several supervised learning algorithms were compared to solve binary classification problems for the surfaces of HAC and extracellular proteins. Then, logistic regression was used for the feature importance analysis of descriptors considering model performance (80.2% accuracy and 87.6% AUC) and interpretability. The HAC proteins showed positive correlations with negatively and positively charged areas but negative correlations with hydrophobicity, the B-factor, the proportion of beta structures, roughness, and the proportion of disordered regions. Finally, the details of each descriptor could be explained concerning adaptative surface strategies of HAC proteins to regulate nonspecific interactions, protein folding, flexibility, stability, and adsorption. This study presented a novel approach using various surface descriptors to identify HAC proteins and provided quantitative design rules for the surfaces well-suited to human cellular crowded environments.

Unraveling the impact of nano-scaling on silicon field-effect transistors for the detection of single-molecules

Electrolyte-gated silicon field-effect transistors (FETs) capable of detecting single molecules could enable high-throughput molecular sensing chips to advance, for example, genomics or proteomics. For solid-gated silicon FETs it is well-known that nano-scaled devices become sensitive to single elementary charges near the silicon-oxide interface. However, in electrolyte-gated FETs, electrolyte screening strongly reduces sensitivity to charges near the gate oxide. The question arises whether nano-scaling electrolyte-gated FETs can entail a sufficiently large signal-to-noise ratio (SNR) for the detection of single molecules. We enhanced a technology computer-aided design tool with electrolyte screening models to calculate the impact of the FET geometry on the single-molecule signal and FET noise. Our continuum FET model shows that a sufficiently large single-molecule SNR is only obtained when nano-scaling all FET channel dimensions. Moreover, we show that the expected scaling trend of the single-molecule SNR breaks down and no longer results in improvements for geometries approaching the decananometer size. This is the characteristic size of the FET channel region modulated by a typical molecule. For gate lengths below 50 nm, the overlap of the modulated region with the highly conductive junctions leads to saturation of the SNR. For cross-sections below 10-30 nm, SNR degrades due to the overlap of the modulated region with the convex FET corners where a larger local gate capacitance reduces charge sensitivity. In our study, assuming a commercial solid-state FET noise amplitude, we find that a suspended nanowire FET architecture with 35 nm length and 5 × 10 nm² cross-section results in the highest SNR of about 10 for a 15-base DNA oligo in a 15 mM electrolyte. In contrast with typical silicon nanowire FET sensors which possess micron-scale gate lengths, we find it to be key that all channel dimensions are scaled down to the decananometer range.

A binary/digital approach to amino acid identification and its application to peptide sequencing and protein identification

A binary/digital method is proposed in theory for the identification of single amino acids (AAs) in the bulk or with a few molecules from a single binary measurement. Combined with Edman degradation (or other cleaving method), it can be used to sequence a peptide or identify the parent protein from a partial sequence. The approach is centered on the superspecificity property of transfer RNAs (tRNAs). Markedly different from conventional and recent single molecule (SM) sequencing methods based on analog measurements, it changes the analytical question 'Which AA is it?' to the much simpler one 'Is there an AA in the detection space?'. Each of 20 terminal residues cleaved from 20 copies of a peptide enters a different cavity with a unique tRNA; tRNA charging (or binding with AA) occurs only in the cavity with the cognate AA. The bound AA or the AA separated from the tRNA is detected with a single binary measurement; its identity is known from the position of the single high bit in the resulting 20-bit output. Alternatively, a 20-stage pipeline can be used with sparse samples. Detection of the bound AA can be done optically by tagging the AAs with a fluorescent dye, or of the freed AA electrically with a nanopore. Necessary conditions for accurate AA identification are satisfied in principle; related computations and simulation results are presented. A modified version that can be used for de novo sequencing in parallel of large numbers of peptides immobilized on a glass slide with the tRNAs carrying a fluorescent tag is also proposed. Both methods can be used for protein identification from partial sequences containing 2 or 3 AA types by using only the corresponding tRNAs. Experiments may be performed to validate them, followed by translation into practice with existing technology; potential implementation issues are discussed. Binary/digital amino acid identification for peptide sequencing.

The emerging landscape of single-molecule protein sequencing technologies

Single-cell profiling methods have had a profound impact on the understanding of cellular heterogeneity. While genomes and transcriptomes can be explored at the single-cell level, single-cell profiling of proteomes is not yet established. Here we describe new single-molecule protein sequencing and identification technologies alongside innovations in mass spectrometry that will eventually enable broad sequence coverage in single-cell profiling. These technologies will in turn facilitate biological discovery and open new avenues for ultrasensitive disease diagnostics.