Predicting Peptide Ionization Efficiencies for Electrospray Ionization Mass Spectrometry Using Machine Learning.

Mass spectrometry (MS) is inherently an information-rich technique. In this era of big data, label-free MS quantification for nontargeted studies has gained increasing popularity, especially for complex systems. One of the cornerstones of successful label-free quantification is the predictive modeling of ionization efficiency (IE) based on solutes' physicochemical properties. While many have studied IE modeling for small molecules, there are limited reports on peptide IEs. In this study, we leverage the stoichiometric relationship in trypsin digests of well-characterized monoclonal antibodies (mAbs) to compile a data set of relative ionization efficiencies (RIEs) for 241 peptides. From each peptide's sequence, we computed a set of physiochemical descriptors, which were then used to train machine learning regression models to predict RIEs. Peptides shorter than 20 amino acids had RIEs that were highly correlated to their molecular weight. A random forest (RF) model was able to best predict the RIEs of a test data set with a mean relative error of 23.9%. For larger peptides, a multilayer perceptron (MLP) model improved RIE prediction compared to current best practices, reducing mean relative error from 60.5% to 32.0%. Finally, we also show the application of the RF model in label-free relative protein quantification and improving the quantification of peptide post-translational modifications (PTMs). This approach to predicting peptide IEs from their sequences enables the development of accurate label-free quantification workflows for peptide and protein analysis.

The origin of the large T c variation in FeSe thin films probed by dual-beam pulsed laser deposition.

FeSe is one of the most enigmatic superconductors. Among the family of iron-based compounds, it has the simplest chemical makeup and structure, and yet it displays superconducting transition temperature ( T c ) spanning 0 to 15 K for thin films, while it is typically 8 K for single crystals. This large variation of T c within one family underscores a key challenge associated with understanding superconductivity in iron chalcogenides. Here, using a dual-beam pulsed laser deposition (PLD) approach, we have fabricated a unique lattice-constant gradient thin film of FeSe which has revealed a clear relationship between the atomic structure and the superconducting transition temperature for the first time. The dual-beam PLD that generates laser fluence gradient inside the plasma plume has resulted in a continuous variation in distribution of edge dislocations within a single film, and a precise correlation between the lattice constant and T c has been observed here, namely, T c ∝ c - c 0 , where c is the c-axis lattice constant (and c 0 is a constant). This explicit relation in conjunction with a theoretical investigation indicates that it is the shifting of the d xy orbital of Fe which plays a governing role in the interplay between nematicity and superconductivity in FeSe. Supplementary Information: The online version contains supplementary material available at 10.1007/s44214-024-00058-0.

Exploring features in chromatographic profiles as a tool for monitoring column performance.

In the biopharmaceutical industry, chromatography resins have a finite number of uses before they start to age and degrade, typically due to losses of ligand integrity and/or density. The "health" of a column is predicted and validated by running multiple cycles on representative scale-down models and can be followed by real-time on-going validation during commercial production. Principal Component Analysis (PCA), Partial Least Square (PLS), Similarity Scores and Single One Point-MultiParameter Technique (SOP-MPT) along with machine learning principles were applied to explore the hypothesis that there is predictive capability of latent variables in chromatography absorbance profiles for process performance (step yield) and product quality (aggregates, fragments, host cell proteins (HCP) and DNA, and Protein A ligand). The first stage of this study is described in this paper: a MabSelect SuRe™ chromatography column was cycled with a method to establish the "normal" baseline for process performance and product quality, followed by runs using a harsher NaOH Cleaning in Place (CIP) procedure (with a higher NaOH concentration than that recommended by the vendor) to accelerate resin degradation. The different mathematical analytical tools correlated with resin degradation of the column (reflected in decreasing step yield and binding capacity with increasing running cycle), specifically when using the Wash, Elution and Strip phases of the chromatography method. Monomer, HCP and DNA content were not significantly impacted and therefore a correlation with product quality was inconsequential. Importantly, this work shows proof-of-concept that while more traditional methods of measuring resin integrity such as the height equivalent to a theoretical place (HETP) and Asymmetry (As) measurements could not detect changes in the integrity of the resin, PCA, PLS, Similarity Scores and SOP-MPT (to a lesser extent) applied to the absorbance data were capable of anticipating issues in the chromatography bed by identifying atypical outcomes.

The 2021 room-temperature superconductivity roadmap.

Designing materials with advanced functionalities is the main focus of contemporary solid-state physics and chemistry. Research efforts worldwide are funneled into a few high-end goals, one of the oldest, and most fascinating of which is the search for an ambient temperature superconductor (A-SC). The reason is clear: superconductivity at ambient conditions implies being able to handle, measure and access a single, coherent, macroscopic quantum mechanical state without the limitations associated with cryogenics and pressurization. This would not only open exciting avenues for fundamental research, but also pave the road for a wide range of technological applications, affecting strategic areas such as energy conservation and climate change. In this roadmap we have collected contributions from many of the main actors working on superconductivity, and asked them to share their personal viewpoint on the field. The hope is that this article will serve not only as an instantaneous picture of the status of research, but also as a true roadmap defining the main long-term theoretical and experimental challenges that lie ahead. Interestingly, although the current research in superconductor design is dominated by conventional (phonon-mediated) superconductors, there seems to be a widespread consensus that achieving A-SC may require different pairing mechanisms.In memoriam, to Neil Ashcroft, who inspired us all.

Perfect Andreev reflection due to the Klein paradox in a topological superconducting state.

In 1928, Dirac proposed a wave equation to describe relativistic electrons1. Shortly afterwards, Klein solved a simple potential step problem for the Dirac equation and encountered an apparent paradox: the potential barrier becomes transparent when its height is larger than the electron energy. For massless particles, backscattering is completely forbidden in Klein tunnelling, leading to perfect transmission through any potential barrier2,3. The recent advent of condensed-matter systems with Dirac-like excitations, such as graphene and topological insulators, has opened up the possibility of observing Klein tunnelling experimentally4-6. In the surface states of topological insulators, fermions are bound by spin-momentum locking and are thus immune from backscattering, which is prohibited by time-reversal symmetry. Here we report the observation of perfect Andreev reflection in point-contact spectroscopy-a clear signature of Klein tunnelling and a manifestation of the underlying 'relativistic' physics of a proximity-induced superconducting state in a topological Kondo insulator. Our findings shed light on a previously overlooked aspect of topological superconductivity and can serve as the basis for a unique family of spintronic and superconducting devices, the interface transport phenomena of which are completely governed by their helical topological states.

Machine-learning guided discovery of a new thermoelectric material.

Thermoelectric technologies are becoming indispensable in the quest for a sustainable future. Recently, an emerging phenomenon, the spin-driven thermoelectric effect (STE), has garnered much attention as a promising path towards low cost and versatile thermoelectric technology with easily scalable manufacturing. However, progress in development of STE devices is hindered by the lack of understanding of the fundamental physics and materials properties responsible for the effect. In such nascent scientific field, data-driven approaches relying on statistics and machine learning, instead of more traditional modeling methods, can exhibit their full potential. Here, we use machine learning modeling to establish the key physical parameters controlling STE. Guided by the models, we have carried out actual material synthesis which led to the identification of a novel STE material with a thermopower an order of magnitude larger than that of the current generation of STE devices.

Nonnegative Matrix Factorization for identification of unknown number of sources emitting delayed signals.

Factor analysis is broadly used as a powerful unsupervised machine learning tool for reconstruction of hidden features in recorded mixtures of signals. In the case of a linear approximation, the mixtures can be decomposed by a variety of model-free Blind Source Separation (BSS) algorithms. Most of the available BSS algorithms consider an instantaneous mixing of signals, while the case when the mixtures are linear combinations of signals with delays is less explored. Especially difficult is the case when the number of sources of the signals with delays is unknown and has to be determined from the data as well. To address this problem, in this paper, we present a new method based on Nonnegative Matrix Factorization (NMF) that is capable of identifying: (a) the unknown number of the sources, (b) the delays and speed of propagation of the signals, and (c) the locations of the sources. Our method can be used to decompose records of mixtures of signals with delays emitted by an unknown number of sources in a nondispersive medium, based only on recorded data. This is the case, for example, when electromagnetic signals from multiple antennas are received asynchronously; or mixtures of acoustic or seismic signals recorded by sensors located at different positions; or when a shift in frequency is induced by the Doppler effect. By applying our method to synthetic datasets, we demonstrate its ability to identify the unknown number of sources as well as the waveforms, the delays, and the strengths of the signals. Using Bayesian analysis, we also evaluate estimation uncertainties and identify the region of likelihood where the positions of the sources can be found.

Topological defect-phase soliton and the pairing symmetry of a two-band superconductor: role of the proximity effect.

We suggest a mechanism which promotes the existence of a phase soliton--a topological defect formed in the relative phase of superconducting gaps of a two-band superconductor with s(+-) type of pairing. This mechanism exploits the proximity effect with a conventional s-wave superconductor which favors the alignment of the phases of the two-band superconductor which, in the case of s(+-) pairing, are π shifted in the absence of proximity. In the case of a strong proximity such an effect can be used to reduce the soliton's energy below the energy of a soliton-free state, thus making the soliton thermodynamically stable. Based on this observation we consider an experimental setup, applicable for both stable and metastable solitons, which can be used to distinguish between ss(+-) and s(++) types of pairing in the iron-based multiband superconductors.