BACKGROUND: Protein Language Models offer a new perspective for addressing challenges in structural biology, while relying solely on sequence information. Recent studies have investigated their effectiveness in forecasting shifts in thermodynamic stability caused by single amino acid mutations, a task known for its complexity due to the sparse availability of data, constrained by experimental limitations. To tackle this problem, we introduce two key novelties: leveraging a Protein Language Model that incorporates Multiple Sequence Alignments to capture evolutionary information, and using a recently released mega-scale dataset with rigorous data pre-processing to mitigate overfitting.
RESULTS: We ensure comprehensive comparisons by fine-tuning various pre-trained models, taking advantage of analyses such as ablation studies and baselines evaluation. Our methodology introduces a stringent policy to reduce the widespread issue of data leakage, rigorously removing sequences from the training set when they exhibit significant similarity with the test set. The MSA Transformer emerges as the most accurate among the models under investigation, given its capability to leverage co-evolution signals encoded in aligned homologous sequences. Moreover, the optimized MSA Transformer outperforms existing methods and exhibits enhanced generalization power, leading to a notable improvement in predicting changes in protein stability resulting from point mutations.
METHODS: Code and data at https://github.com/RitAreaSciencePark/PLM4Muts.
BACKGROUND: Supplementary Information is available at Bioinformatics online.

Three approaches for determining the thermodynamic stability of irreversible processes are described in generalized formulations. The simplest is the Gibbs-Duhem theory, specialized to irreversible trajectories, which uses the concept of virtual displacement in the reverse direction. Its only drawback is that even a trajectory leading to an explosion is identified as a thermodynamically stable motion. In the second approach, we use a thermodynamic Lyapunov function and its time rate from the Lyapunov thermodynamic stability theory (LTS, previously known as CTTSIP). In doing so, we demonstrate that the second differential of entropy, a frequently used Lyapunov function, is useful only for investigating the stability of equilibrium states. Nonequilibrium steady states do not qualify. Without using explicit perturbation coordinates, we further identify asymptotic thermodynamic stability and thermodynamic stability under constantly acting disturbances of unperturbed trajectories as well as of nonequilibrium steady states. The third approach is also based on the Lyapunov function from LTS, but here we additionally use the rates of perturbation coordinates, based on the Gibbs relations and without using their explicit expressions, to identify not only asymptotic thermodynamic stability but also thermodynamic stability under constantly acting disturbances. Only those trajectories leading to an infinite rate of entropy production (unstable states) are excluded from this conclusion. Finally, we use these findings to formulate the Fourth Law of thermodynamics based on the thermodynamic stability. It is a comprehensive statement covering all nonequilibrium trajectories, close to as well as far from equilibrium. Unlike previous suggested \"fourth laws\", this one meets the same level of generality that is associated with the original zeroth to third laws. The above is illustrated using the Schlögl reaction with its multiple steady states in certain regions of operation.

Computational protein sequence design has the ambitious goal of modifying existing or creating new proteins; however, designing stable and functional proteins is challenging without predictability of protein dynamics and allostery. Informing protein design methods with evolutionary information limits the mutational space to more native-like sequences and results in increased stability while maintaining functions. Recently, language models, trained on millions of protein sequences, have shown impressive performance in predicting the effects of mutations. Assessing Rosetta-designed sequences with a language model showed scores that were worse than those of their original sequence. To inform Rosetta design protocols with language model predictions, we added a new metric to restrain the energy function during design using the Evolutionary Scale Modeling (ESM) model. The resulting sequences have better language model scores and similar sequence recovery, with only a minor decrease in the fitness as assessed by Rosetta energy. In conclusion, our work combines the strength of recent machine learning approaches with the Rosetta protein design toolbox.

Enzymes from psychrophilic (cold-loving) organisms have attracted considerable interest over the past decades for their potential in various low-temperature industrial processes. However, we still lack large-scale commercialization of their activities. Here, we review their properties, limitations and potential. Our review is structured around answers to 5 central questions: 1. How do cold-active enzymes achieve high catalytic rates at low temperatures? 2. How is protein flexibility connected to cold-activity? 3. What are the sequence-based and structural determinants for cold-activity? 4. How does the thermodynamic stability of psychrophilic enzymes reflect their cold-active capabilities? 5. How do we effectively identify novel cold-active enzymes, and can we apply them in an industrial context? We conclude that emerging screening technologies combined with big-data handling and analysis make it reasonable to expect a bright future for our understanding and exploitation of cold-active enzymes.

When navigating the environmental exigencies precipitated by global pandemics, the escalation of mask waste presents a multifaceted dilemma. In this avant-garde research, we unveil a novel approach: harnessing the sterilized shredded mask residues (SMRs), predominantly composed of 100 wt. % polypropylene, as pioneering modifiers for asphalt. Distinct proportions of SMR (e.g., 3, 6, and 9 wt. %) were judiciously integrated with fresh-virgin base AP-5 asphalt and subjected to an extensive suite of state-of-the-art examinations, encompassing thin-layer chromatography-flame ionization detection (TLC-FID), Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), and specific rheological metrics. The TLC-FID diagnostic trajectories highlighted the nuanced rejuvenating influence of SMR on the binder, a facet reinforced by a pronounced elevation in the thermodynamic stability index (IC). The FT-IR spectra elucidated SMR\'s preeminent role as a filler, negating notions of chemical reactivity. The TGA analyses unveiled an elevated thermal onset of degradation, signposting enhanced thermal resilience, whereas the DSC readings illuminated a superior thermal comportment at lower extremities. The SEM evaluations rendered a clearer panorama: there was heightened textural perturbation at escalated SMR incorporations, yet the 3 wt. % concoction showcased an optimal, coherent microtexture symbiosis with asphalt. The rheological scrutinies revealed a systematic trajectory: a diminishing penetration and ductility countered by ascending softening points and viscosity metrics. The coup de maître stemmed from the DSR analyses, unequivocally validating SMR\'s unparalleled prowess in curtailing rutting distress. This seminal inquiry not only posits a blueprint for refined pavement longevity but also champions a sustainable countermeasure to pandemic-propelled waste, epitomizing the confluence of environmental prudence an d infrastructural fortitude.

BACKGROUND: Despite GC variation constitutes a fundamental element of genome and species diversity, the precise mechanisms driving it remain unclear. The abundant sequence data available for the ITS2, a commonly employed phylogenetic marker in plants, offers an exceptional resource for exploring the GC variation across angiosperms.
RESULTS: A comprehensive selection of 8666 species, comprising 165 genera, 63 families, and 30 orders were used for the analyses. The alignment of ITS2 sequence-structures and partitioning of secondary structures into paired and unpaired regions were performed using 4SALE. Substitution rates and frequencies among GC base-pairs in the paired regions of ITS2 were calculated using RNA-specific models in the PHASE package. The results showed that the distribution of ITS2 GC contents on the angiosperm phylogeny was heterogeneous, but their increase was generally associated with ITS2 sequence homogenization, thereby supporting the occurrence of GC-biased gene conversion (gBGC) during the concerted evolution of ITS2. Additionally, the GC content in the paired regions of the ITS2 secondary structure was significantly higher than that of the unpaired regions, indicating the selection of GC for thermodynamic stability. Furthermore, the RNA substitution models demonstrated that base-pair transformations favored both the elevation and fixation of GC in the paired regions, providing further support for gBGC.
CONCLUSIONS: Our findings highlight the significance of secondary structure in GC investigation, which demonstrate that both gBGC and structure-based selection are influential factors driving angiosperm ITS2 GC content.

Nanocellulose is an emerging green, biodegradable and biocompatible nanomaterial with negligible toxicities. In this study, a carboxylated nanocellulose (i.e., 2,2,6,6-tetramethylpiperidin-1-oxyl (TEMPO)-oxidized cellulose nanofibril (TEMPO-CNF)) was prepared from corn stover and characterized by X-ray diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR) and differential scanning calorimetry (DSC)/thermogravimetric analysis (TGA). Corn stover-derived TEMPO-CNF was explored as an emulsion co-stabilizer together with Tween 80 for lemongrass essential oil-loaded emulsions. Droplet size, phase behavior and thermodynamic stability of oil-in-water emulsions stabilized by Tween 80 and TEMPO-CNF were investigated. The optimal nanoemulsion stabilized by this binary stabilizer could achieve a mean particle size of 19 nm, and it did not form any phase separation against centrifugal forces, freeze-thaw cycles and at least 30 days of room temperature storage. The nanoencapsulated essential oil had better inhibition activity against the mycelial growth of Aspergillus flavus than pure essential oil. Results from this study demonstrate the potential of using agricultural byproduct-derived nanomaterial as nanoemulsion stabilizers for essential oils with good emulsion thermodynamic stability as well as enhanced antifungal activities.

OBJECTIVE: We conducted a presentation on an 84-year-old male patient who has been diagnosed with TTRA81V (p. TTRA101V) hereditary transthyretin cardiac amyloidosis (hATTR-CM). In order to establish its pathogenicity, we extensively investigated the biochemical and biophysical properties of the condition.
RESULTS: Transthyretin amyloid cardiomyopathy (ATTR-CM) is an increasingly acknowledged progressive infiltrative cardiomyopathy that leads to heart failure and potentially fatal arrhythmias. Gaining a comprehensive understanding of the biochemical and biophysical characteristics of genetically mutated TTR proteins serves as the fundamental cornerstone for delivering precise medical care to individuals affected by ATTR. Laboratory assessments indicated a brain natriuretic peptide of 200.12 ng/L (normal range: 0-100 ng/L) and high-sensitivity cardiac troponin I of 0.189 μg/L (normal range: 0-0.1 μg/L). Echocardiography identified left atrial enlargement, symmetrical left ventricular hypertrophy (16 mm septal and 16 mm posterior wall), and a left ventricular ejection fraction of 56%. Cardiac-enhanced magnetic resonance imaging revealed subendocardial late gadolinium enhancement. Tc-99m-PYP nuclear scintigraphy confirmed grade 3 myocardial uptake, showing an increased heart-to-contralateral ratio (H/CL = 2.33). Genetic testing revealed a heterozygous missense mutation in the TTR gene (c.302C>T), resulting in an alanine-to-valine residue change (p. Ala81Val, following the first 20 residues of signal sequence nomenclature). Biochemical analysis of this variant displayed compromised kinetic stability in both the TTRA81V:WT (wild-type) heterozygote protein (half-life, t1/2  = 21 h) and the TTRA81V homozygote protein (t1/2  = 17.5 h). The kinetic stability fell between that of the TTRWT (t1/2  = 42 h) and the early-onset TTRL55P mutation (t1/2  = 4.4 h), indicating the patient\'s late-onset condition. Kinetic stabilizers (Tafamidis, Diflunisal, and AG10) all exhibited the capacity to inhibit TTRA81V acid- and mechanical force-induced fibril formation, albeit less effectively than with TTRWT. Chromatographic assessment of the patient\'s serum TTR tetramers indicated a slightly lower concentration (3.0 μM) before oral administration of Tafamidis compared with the normal range (3.6-7.2 μM).
CONCLUSIONS: We identified a patient with hATTR-CM who possesses a rare TTRA81V mutation solely associated with cardiac complications. The slightly reduced kinetic stability of this mutation indicates its late-onset nature and contributes to the gradual progression of the disease.

Cymbopogon citratus (DC) stapf. (Gramineae) is a herb known worldwide as lemongrass. The oil obtained, i.e., lemongrass oil has emerged as one among the most relevant natural oils in the pharmaceutical industry owing to its extensive pharmacological and therapeutic benefits including antioxidant, antimicrobial, antiviral and anticancer properties. However, its usage in novel formulations is constrained because of its instability and volatility. To address these concerns, the present study aims to formulate lemongrass-loaded SLN (LGSLN) using hot water titration technique. In the Smix, Tween 80 was selected as a surfactant component, while ethanol was taken as a co-surfactant. Different ratios of Smix (1:1, 1:2, 1:3, 2:1 and 3:1) were utilized to formulate LG-loaded SLN. The results indicated the fact that the LGSLN formulation (abbreviated as LGSLN1), containing lipid phase 10% w/w (i.e., LG 3.33% and SA 6.67%), Tween 80 (20% w/w), ethanol (20% w/w) and distilled water (50% w/w), revealed suitable nanometric size (142.3 ± 5.96 nm) with a high zeta potential value (- 29.12 ± 1.7 mV) and a high entrapment efficiency (77.02 ± 8.12%). A rapid drug release (71.65 ± 5.33%) was observed for LGSLN1 in a time span of 24 h. Additionally, the highest values for steady-state flux (Jss; 0.6133 ± 0.0361 mg/cm2/h), permeability coefficient (Kp; 0.4573 ± 0.0141 (cm/h) × 102) and enhancement ratio (Er; 13.50) was also conferred by LGSLN1. Based on in vitro study results, the developed SLN appeared as a potential carrier for enhanced topical administration of lemongrass oil. The observed results also indicated the fact that the phyto-cosmeceutical prospective of the nanolipidic carrier for topical administration of lemongrass oil utilizing pharmaceutically acceptable components can be explored further for widespread clinical applicability.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s13205-023-03726-5.

Regulatory small RNAs (sRNA) are RNA transcripts that are not translated into proteins but act as functional RNAs. Pathogenic Leptospira cause an epidemic spirochaetal zoonosis, Leptospirosis. It is speculated that Leptospiral sRNAs are involved in orchestrating their pathogenicity. In this study, biocomputational approach was adopted to identify Leptospiral sRNAs. In this study, two sRNA prediction programs, i.e., RNAz and nocoRNAc, were employed to screen the reference genome of Leptospira interrogans serovar Lai. Out of 126 predicted sRNAs, there are 96 cis-antisense sRNAs, 28 trans-encoded sRNAs and 2 sRNAs that partially overlap with protein-coding genes in a sense orientation. To determine whether these candidates are expressed in the pathogen, they were compared with the coverage files generated from our RNA-seq datasets. It was found out that 7 predicted sRNAs are expressed in mid-log phase, stationary phase, serum stress, temperature stress and iron stress while 2 sRNAs are expressed in mid-log phase, stationary phase, serum stress, and temperature stress. Besides, their expressions were also confirmed experimentally via RT-PCR. These experimentally validated candidates were also subjected to mRNA target prediction using TargetRNA2. Taken together, our study demonstrated that biocomputational strategy can serve as an alternative or as a complementary strategy to the laborious and expensive deep sequencing methods not only to uncover putative sRNAs but also to predict their targets in bacteria. In fact, this is the first study that integrates computational approach to predict putative sRNAs in L. interrogans serovar Lai.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s12088-022-01050-9.