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491 papers
Derived data, run outputs and figure source data supporting the article "Joint optimisation of amino acid and coding sequence for de novo designed proteins". Includes the inverse-folding marginals for all 862 backbones and the full 230,992-row double-mutant additivity table.
Overview A prospectively frozen benchmark suite for leakage-safe multi-omic integration. It tests predictive performance, modality utility, model-capacity effects, missingness handling, null behavior, exploratory external transport, failure handling, and compute cost without making claims of clinical utility or causal biological inference. Included datasets Synthetic controls: paired null and planted-signal families with operative structured missingness for binary classification and continuous regression. DepMap/CCLE: transcriptomics, copy number, LC-MS metabolomics, and PRMT5 dependency across 644 cell lines. TCGA BRCA: transcriptomics, copy number, and RPPA protein abundance for ductal-versus-lobular classification across 783 tumors. TCGA LGG, KIRC, and UCEC: transcriptomics and copy number for IDH status, pathological stage, and histology endpoints across 507, 507, and 500 tumors, respectively. CPTAC UCEC exploratory holdout: transcriptomics and copy number for endometrioid-versus-serous transport assessment across 95 patient-disjoint tumors. Methods and controls Nine fixed methods compare Omicau with an unmasked architecture-matched ablation, matched early and single-modality neural controls, early and single-modality linear controls, weighted late fusion, a missingness-only diagnostic, and calibrated latent partial least squares. A TCGA-UCEC complete-training-feature sensitivity tests outcome-associated technical missingness. Internal cohorts use shared group-aware partitions, training-only preprocessing, five outer folds repeated three times, 5,000 paired group bootstraps, paired DeLong tests for AUROC, 4,999 paired squared-error sign flips for R-squared, and Holm adjustment across five primary matched-capacity contrasts. Effect sizes and intervals are the primary evidence. Because repeated out-of-fold predictions share training sets, internal p-values are conditional on the frozen prediction vectors and are not unconditional population-generalization tests. Ten target permutations per cohort are coarse catastrophic-leakage diagnostics, not formal empirical tail-probability tests. TCGA and CPTAC expression scales are harmonized by a source-declared, target-blind transformation with pooled and matched-feature numerical-domain gates. The CPTAC endpoint is exploratory because it was exercised during predeposit development smoke. Literature-anchored controls remain independent of method ranking. Failed, unfavorable, discordant, non-estimable, and indeterminate outcomes remain reportable. Reproducibility The archive contains the frozen protocol, immutable source registry, download and validation code, internal and external partitions, fixed comparator implementations, statistical aggregation, schemas, environment pins, and fault-injection tests. Raw molecular matrices, participant-level data, local paths, and benchmark results are excluded. Deviations Deviation 1 - Aggregation target normalization. Final aggregation converts read-only NumPy memory-mapped target vectors to base NumPy arrays before metric and bootstrap validation while preserving scientific values and frozen randomization streams. Deviation 2 - Comparator and ablation expansion. Matched neural, unmasked, missingness-only, complete-feature, weighted late-fusion, and partial least-squares controls separate fusion value from model capacity, technical missingness, and integration strategy. All settings are fixed before definitive execution. Deviation 3 - Independent external evaluation. A CPTAC UCEC holdout adds 95 patient-disjoint assessment cases. TCGA-UCEC supplies all training and model-selection rows; shared transcriptomic and copy-number features are aligned by unique Entrez identifiers and expression scales are harmonized without using CPTAC outcomes. Deviation 4 - Primary contrast realignment. The primary contrast is Omicau minus the matched early neural control. Prior linear comparisons remain fully reported as contextual estimates and are not substituted for the matched-capacity test. Deviation 5 - External development exposure. The CPTAC endpoint was exercised during predeposit development smoke. External estimates are designated exploratory and are not treated as untouched confirmatory validation. Deviation 6 - External expression-scale correction. Predeposit development smoke exposed incompatible TCGA and CPTAC expression domains. Linear TCGA RSEM values now receive log2(x+1) after negative values are marked missing; CPTAC retains its source-declared log2 scale. Target-blind pooled and matched-feature gates validate compatibility. The correction precedes definitive execution, and earlier smoke outputs are not reused. Deviation 7 - Synthetic missingness application correction. Predeposit audit showed that registered synthetic missingness masks were not reaching model matrices. The masks now alter every synthetic method input exactly as registered. The correction precedes definitive execution, and earlier smoke outputs are not reused.
This research investigates the application of non-linear constraint optimization to biological systems, specifically focusing on optimization of gene expression and protein folding. Traditional optimization methods often struggle with the inherent complexity and dynamic nature of biological systems, necessitating novel approaches that can effectively capture evolutionary principles and adapt to changing conditions. This paper proposes a hybrid method integrating evolutionary algorithms with constraint optimization, incorporating feedback from the system's own adaptive behavior. We demonstrate the effectiveness of this approach in optimizing a simplified biological model, highlighting its potential for broader applicability in the analysis and control of complex biological processes.
E. coli relies on the heat shock response (HSR) to preserve protein homeostasis under stress, through three feedback modules: feedforward translational control, chaperone-mediated sequestration and targeted degradation. Although previous studies have highlighted how this layered architecture ensures rapid and robust protection compared to simpler designs, not much attention is paid to how these modules interact. Moreover, how do interactions among the three modules balance performance trade-offs, where gains in one module may come at the expense of another, yet together yield an optimal overall response? We address this using a mathematical model that integrates protein folding with σ 32 regulation. We show that the feedback modules both cooperate and compete, giving rise to nonmonotonic dynamics that govern HSR performance. Specifically, increasing feedforward strength does accelerate response, but beyond a threshold, despite increasing chaperone levels, it paradoxically slows recovery. Similarly, while sequestration enhances relative chaperone production and per-chaperone efficiency, when excessive, it traps σ 32 in inactive complexes, prolonging recovery and delaying shutdown. Mapping the parameter space reveals regimes of synergy as well as trade-offs between speed and efficiency, with wild-type parameters lying near the optimal region. These results reveal design principles that produces a robust and efficient heat shock response.
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Uremic conditions are common in end-stage kidney disease (ESKD) patients. Accelerated vascular diseases in uremic patients lead to heart failure, stroke, and hypertension. To investigate the effects of uremia on porcine arterial smooth muscle cells (aSMCs), bulk RNA sequencing was used to identify uremia-induced alterations in signaling pathways of aSMCs that might explain the aggressive cardiovascular diseases seen in patients with chronic kidney disease (CKD) and ESKD. Bulk RNA sequencing was performed on porcine aSMCs cultured with serum from normal or uremic pigs. Differentially expressed gene (DEG) analysis revealed that 295 genes were upregulated and 138 genes were downregulated after uremic serum exposure. Gene Ontology molecular function analysis demonstrated that ATP-dependent activity, translation factor activity, and ATP-dependent protein folding chaperones were predicted to be negatively enriched after uremic serum exposure, while proton transmembrane transporter activity, antioxidant activity, and glutathione peroxidase activity were predicted to be positively enriched. Gene set enrichment analysis indicated that the cell cycle was predicted to be negatively enriched after uremic serum exposure in aSMCs. Overrepresentation analysis found that focal adhesion, protein processing in the endoplasmic reticulum (ER) and cell senescence were predicted to be negatively enriched, while lysosome, phagosome, apoptosis, and autophagy were predicted to be positively enriched after uremic serum exposure. This study suggests that the signaling pathways that regulate cellular redox homeostasis, the cellular waste disposal system, ER stress and autophagy are major signaling pathways involved in aSMCs’ responses to uremic serum exposure. These pathways may contribute to the severe arterial-specific clinical symptoms observed in CKD/ESKD patients, such as arterial stiffness, vascular calcification and cardiovascular disease.
Temporal Wave Function Collapse Dynamics explores the theoretical underpinnings of the collapse of temporal wave functions – fundamental units of information within complex systems such as neural networks and protein folding – as a dynamic process. This paper posits that collapse isn't a discrete event but rather a continuous evolution driven by a set of differential equations that capture the interplay between system state, external stimuli, and feedback loops. We propose a novel differential equation system that models this collapse, emphasizing the generation of new, potentially transformative states. This research aims to advance our understanding of complex system behavior by providing a framework for modeling this fundamental process.
Molecular dynamics (MD) simulations are a fundamental tool in materials science, biology, and pharmaceutical research, offering insights into molecular behavior and dynamics. However, traditional MD simulations often suffer from limitations in accuracy and efficiency, particularly when dealing with complex, dynamic environments. This paper introduces a novel algorithm, termed Adaptive Molecular Dynamics Optimization (AMDO), designed to address these challenges by dynamically adjusting simulation parameters based on a learned model. AMDO leverages an adaptive learning algorithm to optimize the simulation process, resulting in enhanced accuracy and reduced computational cost. We demonstrate the effectiveness of AMDO through the simulation of a complex protein folding process, showcasing improved convergence and reduced simulation time compared to conventional MD methods. The core mechanism centers on the continuous adaptation of simulation parameters, enabling the simulation to effectively capture the nuances of molecular interactions.
Colorectal cancer (CRC) is a widespread health issue that attains high mortality. The adaptor protein SH3BP2 amplification results in metabolic changes, oxidative stress, NK cell activity, and inflammation. The NK cells are capable of destroying tumor cells without prior activation, help prevent metastasis, and have prognostic value. Targeting SH3BP2 to regulate NK cell activity in the TME could enhance CRC-based immunotherapy. The cancer hallmark tool helps in understanding SH3BP2 hallmark annotation. Utilizing the STRING tool and the KEGG pathway, protein functional enrichment and PPI networking were analyzed. TIMER 2.0 was used for immune cell infiltration correlation analysis, and UALCAN was used for CPTAC-based protein expression profiling. The GEO (GSE9348) dataset showed SH3BP2 is upregulated in CRC (log2 fold change = 1.18). GEO, TCGA, and cBioPortal revealed SH3BP2 alterations in CRC cases, potentially aiding immune evasion. Mutations in SH3BP2 influence cancer growth, suppressing tumors or promoting them by activating NF-κB and affecting immune responses through WNT/β-catenin, PI3K, MAPK, and JAK-STAT pathways. Overall, SH3BP2 plays a key role in cancer growth and immune regulation, making it a promising target for CRC therapy. Further experimental validation is needed to demonstrate its diagnostic and therapeutic potency.
Researchers at Zhejiang University in China and Imperial College London in the UK built standalone tunnelling electrodes with an average gap of 1.6 nm at the tip of a nanopipette, and used them to identify single nucleotides and proteins electrically. By combining the probes with dielectrophoretic (DEP) trapping, which uses an alternating electric field to concentrate molecules, the team got past the diffusion limit, raising event detection rates by up to five orders of magnitude (100,000-fold) and reaching sub-femtomolar (fM) sensitivity. The paper frames nucleic acid sequencing as a future prospect rather than something achieved here, and notes that at the lowest concentrations the same molecules are likely being recaptured near the probe tip and detected repeatedly. [Quantum Biology Society] Quantum tunnelling, in which an electron passes through a potential barrier it could not classically cross by virtue of its wave nature, has long served as a high-precision measurement tool, because the current varies exponentially with the width of the gap and is therefore sensitive to changes in distance at the atomic scale. Forming a gap narrower than 5 nm between nanoelectrodes and reading the characteristic tunnelling current of a single molecule passing through it has raised hopes for a next generation of nucleic acid sequencing and, potentially, even protein sequencing. Existing approaches based on the scanning tunnelling microscope (STM), however, require a conductive substrate and precise piezo controllers, which makes the system unwieldy. They also have to wait for molecules to diffuse into a gap only nanometres wide, a process that is entropically unfavourable, so the efficiency of real sample analysis has been extremely low. A collaboration led by Longhua Tang at Zhejiang University in China, together with Aleksandar P. Ivanov and Joshua B. Edel at Imperial College London, published a standalone nanoprobe platform in Nature Communications in February 2021 that addresses both limitations at once. ■ Self-Terminating Electrodeposition: Forming Nanometre Gaps With Precision The researchers laser-pulled a theta-shaped dual-barrel quartz capillary into a nanopipette whose tip terminates in two closely spaced nanopores, 25 ± 12 nm in diameter, separated by a quartz septum 15 ± 5 nm wide. Butane was then passed through the pipette and pyrolytically deposited to form two coplanar carbon nanoelectrodes, onto which gold was electrochemically plated. The key is a self-terminating mechanism that applies tunnelling current feedback during plating. The preset current is the sum of a Faradaic deposition component and a tunnelling component. As the gap between the electrodes narrows into the tunnelling regime, the tunnelling component comes to dominate, the Faradaic current falls towards zero, and deposition stops of its own accord. Of the 650 probes fabricated, roughly 85% ran through self-termination successfully. The freshly made gaps were then immersed in ultrapure deionised water for 12 to 48 hours, after which conductance had dropped by an average of 55%, consistent with a widening of the tunnelling gaps. The authors tentatively attribute this change to surface diffusion of gold atoms minimising the total interfacial free energy at a fixed volume. The resulting probes held consistent I-V characteristics over several days. Fitted with the Simmons model, 418 standalone probes had gap widths ranging from sub-nanometre to more than 3 nm, with an average of 1.6 ± 0.6 nm. ■ Verifying Tunnelling by Measuring Solvent Barrier Heights To check that the current flowing across the fabricated gaps really came from tunnelling, the researchers measured the tunnelling potential barrier of the surrounding medium. The values came out at 0.37 ± 0.21 eV for deionised water, 0.78 ± 0.14 eV for dimethyl sulfoxide and 0.97 ± 0.21 eV for hexane, all in agreement with the literature. In air the figure was 1.04 ± 0.83 eV, and the large spread was attributed to possible condensation of water vapour in the gap. In control experiments with bridged, short-circuited junctions, the I-V characteristics showed no dependence on the medium, confirming that the junctions were working as tunnelling junctions rather than through physical contact between the electrodes. ■ Identifying Nucleotides and Proteins by Their Characteristic Conductance Measuring four deoxymononucleotides with a probe of roughly 1.1 nm gap at a bias of 50 mV, the team found a clear ordering in the conductance change (ΔG): dGMP (240 ± 36 nS) > dAMP (180 ± 33 nS) > dCMP (161 ± 5 nS) > dTMP (120 ± 10 nS). The authors offer a partial, qualitative interpretation in terms of the highest occupied molecular orbital (HOMO): dGMP shows the highest conductance because its HOMO level sits closer to the Fermi level of the electrodes. They are careful to add that the actual electron transport mechanism across mononucleotides remains an open question. In a mixed solution of dTMP and dGMP, the two characteristic conductance peaks separated clearly. With a probe of roughly 1.8 nm gap, three proteins chosen for their differing molecular weights and charges were likewise told apart by clearly different conductance values: streptavidin (1.56 ± 0.19 nS), bovine serum albumin (BSA, 1.11 ± 0.21 nS) and immunoglobulin G (IgG, 0.52 ± 0.08 nS). Although the gap was narrower than the proteins themselves, characteristic tunnelling signals were still obtained. ■ Combining Dielectrophoresis: Molecular Trapping and a Five-Order Gain in Detection Rate The biggest obstacle to single-molecule sensing in a nanogap, the mass transport limit, was tackled with dielectrophoresis (DEP). Applying an AC field between the two electrodes (100 kHz, 10 Vpp, 10 seconds) generates exceptionally steep field gradients that pull target molecules in solution towards the probe tip and concentrate them there. Measurements could not be run in parallel, because applying the AC field brought a significant rise in the low-frequency noise associated with conductance fluctuations (flicker noise), along with a milder increase in higher-frequency noise attributed to capacitance. The team therefore ran DEP concentration and DC tunnelling detection sequentially. At a poly-A20 DNA concentration of 1 fM, no significant tunnelling events were seen without DEP, whereas clear spike signals appeared after trapping, and event detection rates rose by up to five orders of magnitude. That brought detection down to 0.1 fM for poly-A20 and 0.15 fM for streptavidin. ■ Significance and Limits: Single-Molecule Identification and the Recapture Mechanism The significance of the work lies in realising an ultrasensitive nanoscale tunnelling sensor that operates in solution as a standalone capillary probe, breaking away from the conventional STM architecture in which a conductive substrate is essential. The authors also state that, to their knowledge, this is the first example of dynamic control of molecular transport used in any tunnelling system. The paper treats nucleic acid sequencing as a future prospect rather than something completed here; what is demonstrated is the identification of single mononucleotides and the detection of oligomers and proteins. The authors also note that at the lowest concentrations probed, around 0.1 fM, the event rate is very high, above 200 events per second, and loses any significant dependence on concentration. They explain this as likely arising because trapped molecules localise and accumulate around the tip, so the same molecules have a much higher probability of being recaptured and interacting with the gap, and are probably detected multiple times. Read that way, in this regime the platform looks less like a quantitative counting instrument and more like an ultrasensitive qualitative test for the presence of trace molecules. #QuantumTunnelling #Dielectrophoresis #SingleMoleculeDetection #Nanoelectrode #TunnellingCurrent #Nucleotide #ProteinConductance #SimmonsModel #Nanopipette #BiomolecularSensing #QuantumBiology #NatureCommunications Source (Nature Communications): https://www.nature.com/articles/s41467-021-21101-x Follow-up study (Sci. Adv. 2022): https://doi.org/10.1126/sciadv.abm8149
Researchers at Zhejiang University in China and Imperial College London in the UK built standalone tunnelling electrodes with an average gap of 1.6 nm at the tip of a nanopipette, and used them to identify single nucleotides and proteins electrically. By combining the probes with dielectrophoretic (DEP) trapping, which uses an alternating electric field to concentrate molecules, the team got past the diffusion limit, raising event detection rates by up to five orders of magnitude (100,000-fold) and reaching sub-femtomolar (fM) sensitivity. The paper frames nucleic acid sequencing as a future prospect rather than something achieved here, and notes that at the lowest concentrations the same molecules are likely being recaptured near the probe tip and detected repeatedly. [Quantum Biology Society] Quantum tunnelling, in which an electron passes through a potential barrier it could not classically cross by virtue of its wave nature, has long served as a high-precision measurement tool, because the current varies exponentially with the width of the gap and is therefore sensitive to changes in distance at the atomic scale. Forming a gap narrower than 5 nm between nanoelectrodes and reading the characteristic tunnelling current of a single molecule passing through it has raised hopes for a next generation of nucleic acid sequencing and, potentially, even protein sequencing. Existing approaches based on the scanning tunnelling microscope (STM), however, require a conductive substrate and precise piezo controllers, which makes the system unwieldy. They also have to wait for molecules to diffuse into a gap only nanometres wide, a process that is entropically unfavourable, so the efficiency of real sample analysis has been extremely low. A collaboration led by Longhua Tang at Zhejiang University in China, together with Aleksandar P. Ivanov and Joshua B. Edel at Imperial College London, published a standalone nanoprobe platform in Nature Communications in February 2021 that addresses both limitations at once. ■ Self-Terminating Electrodeposition: Forming Nanometre Gaps With Precision The researchers laser-pulled a theta-shaped dual-barrel quartz capillary into a nanopipette whose tip terminates in two closely spaced nanopores, 25 ± 12 nm in diameter, separated by a quartz septum 15 ± 5 nm wide. Butane was then passed through the pipette and pyrolytically deposited to form two coplanar carbon nanoelectrodes, onto which gold was electrochemically plated. The key is a self-terminating mechanism that applies tunnelling current feedback during plating. The preset current is the sum of a Faradaic deposition component and a tunnelling component. As the gap between the electrodes narrows into the tunnelling regime, the tunnelling component comes to dominate, the Faradaic current falls towards zero, and deposition stops of its own accord. Of the 650 probes fabricated, roughly 85% ran through self-termination successfully. The freshly made gaps were then immersed in ultrapure deionised water for 12 to 48 hours, after which conductance had dropped by an average of 55%, consistent with a widening of the tunnelling gaps. The authors tentatively attribute this change to surface diffusion of gold atoms minimising the total interfacial free energy at a fixed volume. The resulting probes held consistent I-V characteristics over several days. Fitted with the Simmons model, 418 standalone probes had gap widths ranging from sub-nanometre to more than 3 nm, with an average of 1.6 ± 0.6 nm. ■ Verifying Tunnelling by Measuring Solvent Barrier Heights To check that the current flowing across the fabricated gaps really came from tunnelling, the researchers measured the tunnelling potential barrier of the surrounding medium. The values came out at 0.37 ± 0.21 eV for deionised water, 0.78 ± 0.14 eV for dimethyl sulfoxide and 0.97 ± 0.21 eV for hexane, all in agreement with the literature. In air the figure was 1.04 ± 0.83 eV, and the large spread was attributed to possible condensation of water vapour in the gap. In control experiments with bridged, short-circuited junctions, the I-V characteristics showed no dependence on the medium, confirming that the junctions were working as tunnelling junctions rather than through physical contact between the electrodes. ■ Identifying Nucleotides and Proteins by Their Characteristic Conductance Measuring four deoxymononucleotides with a probe of roughly 1.1 nm gap at a bias of 50 mV, the team found a clear ordering in the conductance change (ΔG): dGMP (240 ± 36 nS) > dAMP (180 ± 33 nS) > dCMP (161 ± 5 nS) > dTMP (120 ± 10 nS). The authors offer a partial, qualitative interpretation in terms of the highest occupied molecular orbital (HOMO): dGMP shows the highest conductance because its HOMO level sits closer to the Fermi level of the electrodes. They are careful to add that the actual electron transport mechanism across mononucleotides remains an open question. In a mixed solution of dTMP and dGMP, the two characteristic conductance peaks separated clearly. With a probe of roughly 1.8 nm gap, three proteins chosen for their differing molecular weights and charges were likewise told apart by clearly different conductance values: streptavidin (1.56 ± 0.19 nS), bovine serum albumin (BSA, 1.11 ± 0.21 nS) and immunoglobulin G (IgG, 0.52 ± 0.08 nS). Although the gap was narrower than the proteins themselves, characteristic tunnelling signals were still obtained. ■ Combining Dielectrophoresis: Molecular Trapping and a Five-Order Gain in Detection Rate The biggest obstacle to single-molecule sensing in a nanogap, the mass transport limit, was tackled with dielectrophoresis (DEP). Applying an AC field between the two electrodes (100 kHz, 10 Vpp, 10 seconds) generates exceptionally steep field gradients that pull target molecules in solution towards the probe tip and concentrate them there. Measurements could not be run in parallel, because applying the AC field brought a significant rise in the low-frequency noise associated with conductance fluctuations (flicker noise), along with a milder increase in higher-frequency noise attributed to capacitance. The team therefore ran DEP concentration and DC tunnelling detection sequentially. At a poly-A20 DNA concentration of 1 fM, no significant tunnelling events were seen without DEP, whereas clear spike signals appeared after trapping, and event detection rates rose by up to five orders of magnitude. That brought detection down to 0.1 fM for poly-A20 and 0.15 fM for streptavidin. ■ Significance and Limits: Single-Molecule Identification and the Recapture Mechanism The significance of the work lies in realising an ultrasensitive nanoscale tunnelling sensor that operates in solution as a standalone capillary probe, breaking away from the conventional STM architecture in which a conductive substrate is essential. The authors also state that, to their knowledge, this is the first example of dynamic control of molecular transport used in any tunnelling system. The paper treats nucleic acid sequencing as a future prospect rather than something completed here; what is demonstrated is the identification of single mononucleotides and the detection of oligomers and proteins. The authors also note that at the lowest concentrations probed, around 0.1 fM, the event rate is very high, above 200 events per second, and loses any significant dependence on concentration. They explain this as likely arising because trapped molecules localise and accumulate around the tip, so the same molecules have a much higher probability of being recaptured and interacting with the gap, and are probably detected multiple times. Read that way, in this regime the platform looks less like a quantitative counting instrument and more like an ultrasensitive qualitative test for the presence of trace molecules. #QuantumTunnelling #Dielectrophoresis #SingleMoleculeDetection #Nanoelectrode #TunnellingCurrent #Nucleotide #ProteinConductance #SimmonsModel #Nanopipette #BiomolecularSensing #QuantumBiology #NatureCommunications Source (Nature Communications): https://www.nature.com/articles/s41467-021-21101-x Follow-up study (Sci. Adv. 2022): https://doi.org/10.1126/sciadv.abm8149
Derived data, run outputs and figure source data supporting the article "Joint optimisation of amino acid and coding sequence for de novo designed proteins". Includes the inverse-folding marginals for all 862 backbones and the full 230,992-row double-mutant additivity table.
Alfalfa biomass contains significant carbohydrate fractions underutilized in animal feed. This research optimized the enzymatic hydrolysis of Alfalfa biomass and evaluated single-cell protein (SCP) production using Candida utilis and Komagataella pastoris . Hydrolysis kinetics in this study showed biphasic sugar release, with enzymatic optimization increasing monomeric sugar yield from 18.5% to a maximum of 33.0% (a 78% relative increase) at 22 mg protein/g biomass enzyme loading. C. utilis demonstrated superior metabolic versatility, consuming 23% more total sugars than K. pastoris , with particularly enhanced pentose utilization showing 45% greater xylose consumption. K. pastoris achieved 51% faster growth rates than C. utilis , while both yeasts produced comparable protein yields per unit sugar consumed. SCP production enriched protein content 2.1-fold compared to raw Alfalfa biomass, reaching approximately 40% crude protein. Essential amino acid profiling (tryptophan excluded) showed that fermentation substantially improved several essential amino acids relative to FAO/WHO reference values, particularly lysine, threonine, and methionine; however, methionine + cysteine remained below the FAO reference ratio in both yeasts ( K. pastoris : 0.99; C. utilis : 0.87), indicating that sulfur-containing amino acids remain a limiting factor despite overall nutritional improvement. The integrated bioprocess achieved 27.6% carbohydrate-to-biomass conversion efficiency and 11.3% carbohydrate-to-protein conversion efficiency based on measured monomeric sugars (oligosaccharide utilization during fermentation was not independently quantified). This work demonstrates a promising lab-scale strategy for alfalfa valorization through enzymatic hydrolysis and yeast fermentation, yielding a nutritionally improved protein product; techno-economic analysis, feeding trials, and scale-up studies are required before cost-effectiveness or industrial readiness can be established.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
Scientists are using AlphaFold to strengthen a photosynthesis enzyme for resilient, heat-tolerant crops.
Explore how AlphaFold has accelerated science and fueled a global wave of biological discovery.
AlphaFold has revealed the structure of a key protein behind heart disease
