Vicia faba Peptide Network Supplementation Does Not Differ From Milk Protein in Modulating Changes in Muscle Size During Short-Term Immobilization and Subsequent Remobilization, but Increases Muscle Protein Synthesis Rates During Remobilization in Healthy Young Men.

BACKGROUND: Muscle mass and strength decrease during short periods of immobilization and slowly recover during remobilization. Recent artificial intelligence applications have identified peptides that appear to possess anabolic properties in in vitro assays and murine models. OBJECTIVES: This study aimed to compare the impact of Vicia faba peptide network compared with milk protein supplementation on muscle mass and strength loss during limb immobilization and regain during remobilization. METHODS: Thirty young (24 ± 5 y) men were subjected to 7 d of one-legged knee immobilization followed by 14 d of ambulant recovery. Participants were randomly allocated to ingest either 10 g of the Vicia faba peptide network (NPN_1; n = 15) or an isonitrogenous control (milk protein concentrate; MPC; n = 15) twice daily throughout the study. Single-slice computed tomography scans were performed to assess quadriceps cross-sectional area (CSA). Deuterium oxide ingestion and muscle biopsy sampling were applied to measure myofibrillar protein synthesis rates. RESULTS: Leg immobilization decreased quadriceps CSA (primary outcome) from 81.9 ± 10.6 to 76.5 ± 9.2 cm2 and from 74.8 ± 10.6 to 71.5 ± 9.8 cm2 in the NPN_1 and MPC groups, respectively (P < 0.001). Remobilization partially recovered quadriceps CSA (77.3 ± 9.3 and 72.6 ± 10.0 cm2, respectively; P = 0.009), with no differences between the groups (P > 0.05). During immobilization, myofibrillar protein synthesis rates (secondary outcome) were lower in the immobilized leg (1.07% ± 0.24% and 1.10% ± 0.24%/d, respectively) than in the non-immobilized leg (1.55% ± 0.27% and 1.52% ± 0.20%/d, respectively; P < 0.001), with no differences between the groups (P > 0.05). During remobilization, myofibrillar protein synthesis rates in the immobilized leg were greater with NPN_1 than those with MPC (1.53% ± 0.38% vs. 1.23% ± 0.36%/d, respectively; P = 0.027). CONCLUSION: NPN_1 supplementation does not differ from milk protein in modulating the loss of muscle size during short-term immobilization and the regain during remobilization in young men. NPN_1 supplementation does not differ from milk protein supplementation in modulating the myofibrillar protein synthesis rates during immobilization but further increases myofibrillar protein synthesis rates during remobilization.

Improved Strength Recovery and Reduced Fatigue with Suppressed Plasma Myostatin Following Supplementation of a Vicia faba Hydrolysate, in a Healthy Male Population.

Delayed onset muscle soreness (DOMS) due to intense physical exertion can negatively impact contractility and performance. Previously, NPN_1 (PeptiStrong™), a Vicia faba hydrolysate derived from a protein concentrate discovered through artificial intelligence (AI), was preclinically shown to help maintain muscle health, indicating the potential to mediate the effect of DOMS and alter molecular markers of muscle damage to improve recovery and performance. A randomised double-blind placebo-controlled trial was conducted on 30 healthy male (30-45 years old) volunteers (NCT05159375). Following initial strength testing on day 0, subjects were administered either placebo or NPN_1 (2.4 g/day). On day 14, DOMS was induced using resistance exercise. Strength recovery and fatigue were measured after 48 and 72 h. Biomarker analysis was performed on blood samples collected prior to DOMS induction and 0, 2, 48 and 72 h post-DOMS induction. NPN_1 supplementation significantly improved strength recovery compared to placebo over the 72 h period post-resistance exercise (p = 0.027), measured by peak torque per bodyweight, but not at individual timepoints. Muscle fatigue was significantly reduced over the same 72 h period (p = 0.041), as was myostatin expression (p = 0.006). A concomitant increase in other acute markers regulating muscle protein synthesis, regeneration and myoblast differentiation was also observed. NPN_1 significantly improves strength recovery and restoration, reduces fatigue and positively modulates alterations in markers related to muscle homeostasis.

Artificial Intelligence in Functional Food Ingredient Discovery and Characterisation: A Focus on Bioactive Plant and Food Peptides.

Scientific research consistently demonstrates that diseases may be delayed, treated, or even prevented and, thereby, health may be maintained with health-promoting functional food ingredients (FFIs). Consumers are increasingly demanding sound information about food, nutrition, nutrients, and their associated health benefits. Consequently, a nutrition industry is being formed around natural foods and FFIs, the economic growth of which is increasingly driven by consumer decisions. Information technology, in particular artificial intelligence (AI), is primed to vastly expand the pool of characterised and annotated FFIs available to consumers, by systematically discovering and characterising natural, efficacious, and safe bioactive ingredients (bioactives) that address specific health needs. However, FFI-producing companies are lagging in adopting AI technology for their ingredient development pipelines for several reasons, resulting in a lack of efficient means for large-scale and high-throughput molecular and functional ingredient characterisation. The arrival of the AI-led technological revolution allows for the comprehensive characterisation and understanding of the universe of FFI molecules, enabling the mining of the food and natural product space in an unprecedented manner. In turn, this expansion of bioactives dramatically increases the repertoire of FFIs available to the consumer, ultimately resulting in bioactives being specifically developed to target unmet health needs.

Preservatives from food-For food: Pea protein hydrolysate as a novel bio-preservative against Escherichia coli O157:H7 on a lettuce leaf.

Fresh-cut fruits and vegetables are becoming particularly popular as healthy fast-food options; however, they present challenges such as accelerated rates of decay and increased risk for contamination when compared to whole produce. Given that food safety must remain paramount for producers and manufacturers, research into novel, natural food preservation solutions which can help to ensure food safety and protect against spoilage is on the rise. In this work, we investigated the potential of using a novel protein hydrolysate, produced by the enzymatic hydrolysis of Pisum sativum (PSH), as a novel bio-preservative and its abilities to reduce populations of Escherichia coli O157:H7 after inoculation on a lettuce leaf. While unhydrolyzed P. sativum proteins show no antimicrobial activity, once digested, and purified, the enzymatically released peptides induced in vitro bactericidal effects on the foodborne pathogen at 8 mg/ml. When applied on an infected lettuce leaf, the PSH significantly reduced the number of bacteria recovered after 2 hr of treatment. PSH may be preferred over other preservation strategies based on its natural, inexpensive, sustainable source, environmentally friendly process, nontoxic nature, good batch to batch consistency, and ability to significantly reduce counts of E. coli both in vitro and in a lettuce leaf.

An Artificial-Intelligence-Discovered Functional Ingredient, NRT_N0G5IJ, Derived from Pisum sativum, Decreases HbA1c in a Prediabetic Population.

The prevalence of prediabetes is rapidly increasing, and this can lead to an increased risk for individuals to develop type 2 diabetes and associated diseases. Therefore, it is necessary to develop nutritional strategies to maintain healthy glucose levels and prevent glucose metabolism dysregulation in the general population. Functional ingredients offer great potential for the prevention of various health conditions, including blood glucose regulation, in a cost-effective manner. Using an artificial intelligence (AI) approach, a functional ingredient, NRT_N0G5IJ, was predicted and produced from Pisum sativum (pea) protein by hydrolysis and then validated. Treatment of human skeletal muscle cells with NRT_N0G5IJ significantly increased glucose uptake, indicating efficacy of this ingredient in vitro. When db/db diabetic mice were treated with NRT_N0G5IJ, we observed a significant reduction in glycated haemoglobin (HbA1c) levels and a concomitant benefit on fasting glucose. A pilot double-blinded, placebo controlled human trial in a population of healthy individuals with elevated HbA1c (5.6% to 6.4%) showed that HbA1c percentage was significantly reduced when NRT_N0G5IJ was supplemented in the diet over a 12-week period. Here, we provide evidence of an AI approach to discovery and demonstrate that a functional ingredient identified using this technology could be used as a supplement to maintain healthy glucose regulation.

Characterising the efficacy and bioavailability of bioactive peptides identified for attenuating muscle atrophy within a Vicia faba-derived functional ingredient.

Characterising key components within functional ingredients as well as assessing efficacy and bioavailability is an important step in validating nutritional interventions. Machine learning can assess large and complex data sets, such as proteomic data from plants sources, and so offers a prime opportunity to predict key bioactive components within a larger matrix. Using machine learning, we identified two potentially bioactive peptides within a Vicia faba derived hydrolysate, NPN_1, an ingredient which was previously identified for preventing muscle loss in a murine disuse model. We investigated the predicted efficacy of these peptides in vitro and observed that HLPSYSPSPQ and TIKIPAGT were capable of increasing protein synthesis and reducing TNF-α secretion, respectively. Following confirmation of efficacy, we assessed bioavailability and stability of these predicted peptides and found that as part of NPN_1, both HLPSYSPSPQ and TIKIPAGT survived upper gut digestion, were transported across the intestinal barrier and exhibited notable stability in human plasma. This work is a first step in utilising machine learning to untangle the complex nature of functional ingredients to predict active components, followed by subsequent assessment of their efficacy, bioavailability and human plasma stability in an effort to assist in the characterisation of nutritional interventions.

Discovery through Machine Learning and Preclinical Validation of Novel Anti-Diabetic Peptides.

While there have been significant advances in drug discovery for diabetes mellitus over the past couple of decades, there is an opportunity and need for improved therapies. While type 2 diabetic patients better manage their illness, many of the therapeutics in this area are peptide hormones with lengthy sequences and a molecular structure that makes them challenging and expensive to produce. Using machine learning, we present novel anti-diabetic peptides which are less than 16 amino acids in length, distinct from human signalling peptides. We validate the capacity of these peptides to stimulate glucose uptake and Glucose transporter type 4 (GLUT4) translocation in vitro. In obese insulin-resistant mice, predicted peptides significantly lower plasma glucose, reduce glycated haemoglobin and even improve hepatic steatosis when compared to treatments currently in use in a clinical setting. These unoptimised, linear peptides represent promising candidates for blood glucose regulation which require further evaluation. Further, this indicates that perhaps we have overlooked the class of natural short linear peptides, which usually come with an excellent safety profile, as therapeutic modalities.

An Artificial Intelligence Characterised Functional Ingredient, Derived from Rice, Inhibits TNF-α and Significantly Improves Physical Strength in an Inflammaging Population.

Food-derived bioactive peptides offer great potential for the treatment and maintenance of various health conditions, including chronic inflammation. Using in vitro testing in human macrophages, a rice derived functional ingredient natural peptide network (NPN) significantly reduced Tumour Necrosis Factor (TNF)-α secretion in response to lipopolysaccharides (LPS). Using artificial intelligence (AI) to characterize rice NPNs lead to the identification of seven potentially active peptides, the presence of which was confirmed by liquid chromatography tandem mass spectrometry (LC-MS/MS). Characterization of this network revealed the constituent peptides displayed anti-inflammatory properties as predicted in vitro. The rice NPN was then tested in an elderly "inflammaging" population with a view to subjectively assess symptoms of digestive discomfort through a questionnaire. While the primary subjective endpoint was not achieved, analysis of objectively measured physiological and physical secondary readouts showed clear significant benefits on the ability to carry out physical challenges such as a chair stand test that correlated with a decrease in blood circulating TNF-α. Importantly, the changes observed were without additional exercise or specific dietary alterations. Further health benefits were reported such as significant improvement in glucose control, a decrease in serum LDL concentration, and an increase in HDL concentration; however, this was compliance dependent. Here we provide in vitro and human efficacy data for a safe immunomodulatory functional ingredient characterized by AI.

Preclinical Evaluation of a Food-Derived Functional Ingredient to Address Skeletal Muscle Atrophy.

Skeletal muscle is the metabolic powerhouse of the body, however, dysregulation of the mechanisms involved in skeletal muscle mass maintenance can have devastating effects leading to many metabolic and physiological diseases. The lack of effective solutions makes finding a validated nutritional intervention an urgent unmet medical need. In vitro testing in murine skeletal muscle cells and human macrophages was carried out to determine the effect of a hydrolysate derived from vicia faba (PeptiStrong: NPN_1) against phosphorylated S6, atrophy gene expression, and tumour necrosis factor alpha (TNF-α) secretion, respectively. Finally, the efficacy of NPN_1 on attenuating muscle waste in vivo was assessed in an atrophy murine model. Treatment of NPN_1 significantly increased the phosphorylation of S6, downregulated muscle atrophy related genes, and reduced lipopolysaccharide-induced TNF-α release in vitro. In a disuse atrophy murine model, following 18 days of NPN_1 treatment, mice exhibited a significant attenuation of muscle loss in the soleus muscle and increased the integrated expression of Type I and Type IIa fibres. At the RNA level, a significant upregulation of protein synthesis-related genes was observed in the soleus muscle following NPN_1 treatment. In vitro and preclinical results suggest that NPN_1 is an effective bioactive ingredient with great potential to prolong muscle health.

Human milk and infant formulae: Peptide differences and the opportunity to address the functional gap.

Bovine-derived formula milk (FM) is a common substitute to human milk (HM), but lacks key functional benefits associated with HM. Accordingly, there have been significant efforts to humanise FM. Recent research has demonstrated that HM-derived peptides convey an array of beneficial bioactivities. Given that peptides serve as important signalling molecules offering high specificity and potency, they represent a prime opportunity to humanise FM. To further understand how HM-derived peptides contribute to infant health, we used peptidomics and bioinformatics to compare the peptide profile of HM to commercially available FM. We found clear and substantial differences between HM and FM in terms of peptide physicochemical properties, protein coverage and abundance. We additionally identified 618 peptides specific to HM that represent an important untapped source to be explored for novel bioactivities. While further study is required, our findings highlight the potential of a peptide-based approach to address the functional gap in FM.

Unlocking NuriPep 1653 From Common Pea Protein: A Potent Antimicrobial Peptide to Tackle a Pan-Drug Resistant Acinetobacter baumannii.

While the antibiotic era has come and gone, antimicrobial peptides (AMPs) hold promise as novel therapies to treat multidrug resistant (MDR) pathogens in an age where the threat of multidrug resistance escalates worldwide. Here, we report the bactericidal properties of NuriPep 1653, a novel 22 mer and non-modified peptide. NuriPep 1653 was identified within the sequence of the non-antimicrobial P54 protein, which is involved in nutrient reservoir activity in Pisum sativum. Total bacterial clearance of Acinetobacter baumannii cells (1 × 108 cells/mL) was observed using only 4 × MIC (48 µg/mL) of NuriPep 1653 after just 20 min of treatment. We uncovered a synergistic interaction between NuriPep 1653 and another antimicrobial peptide, colistin. The MIC of NuriPep 1653 and colistin dropped from 12 and 8 µg/mL to 2 and 1 µg/mL, respectively, when they were combined. NuriPep 1653 exhibits no cytotoxicity in different human cell lines and has a low propensity to induce bacterial resistance in a colistin resistant clinical isolate of A. baumannii. The existence of these peptides embedded in proteins unearths potentially new classes of antimicrobials with activity against clinically relevant pathogens. Our findings push the boundaries of traditional peptide discovery and represent a leading edge for natural bioactive compounds which may have a common existence in nature but remain unexposed.

Following the digestion of milk proteins from mother to baby.

Little is known about the digestive process in infants. In particular, the chronological activity of enzymes across the course of digestion in the infant remains largely unknown. To create a temporal picture of how milk proteins are digested, enzyme activity was compared between intact human milk samples from three mothers and the gastric samples from each of their 4-12 day postpartum infants, 2 h after breast milk ingestion. The activities of 7 distinct enzymes are predicted in the infant stomach based on their observed cleavage pattern in peptidomics data. We found that the same patterns of cleavage were evident in both intact human milk and gastric milk samples, demonstrating that the enzyme activities that begin in milk persist in the infant stomach. However, the extent of enzyme activity is found to vary greatly between the intact milk and gastric samples. Overall, we observe that milk-specific proteins are cleaved at higher levels in the stomach compared to human milk. Notably, the enzymes we predict here only explain 78% of the cleavages uniquely observed in the gastric samples, highlighting that further investigation of the specific enzyme activities associated with digestion in infants is warranted.

A peptidomic analysis of human milk digestion in the infant stomach reveals protein-specific degradation patterns.

In vitro digestion of isolated milk proteins results in milk peptides with a variety of actions. However, it remains unclear to what degree protein degradation occurs in vivo in the infant stomach and whether peptides previously annotated for bioactivity are released. This study combined nanospray LC separation with time-of-flight mass spectrometry, comprehensive structural libraries, and informatics to analyze milk from 3 human mothers and the gastric aspirates from their 4- to 12-d-old postpartum infants. Milk from the mothers contained almost 200 distinct peptides, demonstrating enzymatic degradation of milk proteins beginning either during lactation or between milk collection and feeding. In the gastric samples, 649 milk peptides were identified, demonstrating that digestion continues in the infant stomach. Most peptides in both the intact milk and gastric samples were derived from ß-casein. The numbers of peptides from ß-casein, lactoferrin, α-lactalbumin, lactadherin, κ-casein, serum albumin, bile salt-associated lipase, and xanthine dehydrogenase/oxidase were significantly higher in the gastric samples than in the milk samples (P < 0.05). A total of 603 peptides differed significantly in abundance between milk and gastric samples (P < 0.05). Most of the identified peptides have previously identified biologic activity. Gastric proteolysis occurs in the term infant in the first 2 wk of life, releasing biologically active milk peptides with immunomodulatory and antibacterial properties of clinical relevance to the proximal intestinal tract. Data are available via ProteomeXchange (identifier PXD000688).

Predicting the important enzymes in human breast milk digestion.

Human milk is known to contain several proteases, but little is known about whether these enzymes are active, which proteins they cleave, and their relative contribution to milk protein digestion in vivo. This study analyzed the mass spectrometry-identified protein fragments found in pooled human milk by comparing their cleavage sites with the enzyme specificity patterns of an array of enzymes. The results indicate that several enzymes are actively taking part in the digestion of human milk proteins within the mammary gland, including plasmin and/or trypsin, elastase, cathepsin D, pepsin, chymotrypsin, a glutamyl endopeptidase-like enzyme, and proline endopeptidase. Two proteins were most affected by enzyme hydrolysis: ß-casein and polymeric immunoglobulin receptor. In contrast, other highly abundant milk proteins such as α-lactalbumin and lactoferrin appear to have undergone no proteolytic cleavage. A peptide sequence containing a known antimicrobial peptide is released in breast milk by elastase and cathepsin D.

Amino acid enrichment and compositional changes among mammalian milk proteins and the resulting nutritional consequences.

Milk is a hallmark of mammalian evolution: a unique food that has evolved with mammals. Despite the importance of this food, it is not known if variation in AA composition between different species is important to milk proteins or how it might affect the nutritional value of milk. As milk is the only food source for newborn mammals, it has long been speculated that milk proteins should be enriched in essential AA. However, no systematic analysis supports this assumption. Although many factors influence the overall nutritional value of milk, including total protein concentration, we focused here on the AA composition of milk proteins and investigated the possibility that selection drives compositional changes. We identified 9 major milk proteins present in 13 mammalian species and compared them with a large group of nonmilk proteins. Our results indicate heterogeneity in the AA composition of milk proteins, showing significant enrichment and depletion of certain AA in milk-specific proteins. Although high levels of particular AA appear to be consistently maintained, orthologous milk proteins display significant differences in AA composition across species, most notably among the caseins. Interspecies variation of milk composition is thought to be indicative of nutritional optimization to the requirements of the species. In accordance with this, our observations indicate that milk proteins may have adapted to the species-specific nutritional needs of the neonate.

Extensive in vivo human milk peptidomics reveals specific proteolysis yielding protective antimicrobial peptides.

Milk is traditionally considered an ideal source of the basic elemental nutrients required by infants. More detailed examination is revealing that milk represents a more functional ensemble of components with benefits to both infants and mothers. A comprehensive peptidomics method was developed and used to analyze human milk yielding an extensive array of protein products present in the fluid. Over 300 milk peptides were identified originating from major and many minor protein components of milk. As expected, the majority of peptides derived from ß-casein, however no peptide fragments from the major milk proteins lactoferrin, α-lactalbumin, and secretory immunoglobulin A were identified. Proteolysis in the mammary gland is selective-released peptides were drawn only from specific proteins and typically from only select parts of the parent sequence. A large number of the peptides showed significant sequence overlap with peptides with known antimicrobial or immunomodulatory functions. Antibacterial assays showed the milk peptide mixtures inhibited the growth of Escherichia coli and Staphylococcus aureus . The predigestion of milk proteins and the consequent release of antibacterial peptides may provide a selective advantage through evolution by protecting both the mother's mammary gland and her nursing offspring from infection.

EnzymePredictor: a tool for predicting and visualizing enzymatic cleavages of digested proteins.

Mass spectrometric analysis of peptides contained in enzymatically digested hydrolysates of proteins is increasingly being used to characterize potentially bioactive or otherwise interesting hydrolysates. However, when preparations containing mixtures of enzymes are used, from either biological or experimental sources, it is unclear which of these enzymes have been most important in hydrolyzing the sample. We have developed a tool to rapidly evaluate the evidence for which enzymes are most likely to have cleaved the sample. EnzymePredictor, a web-based software, has been developed to (i) identify the protein sources of fragments found in the hydrolysates and map them back on it, (ii) identify enzymes that could yield such cleavages, and (iii) generate a colored visualization of the hydrolysate, the source proteins, the fragments, and the predicted enzymes. It tabulates the enzymes ranked according to their cleavage counts. The provision of odds ratio and standard error in the table permits users to evaluate how distinctively particular enzymes may be favored over other enzymes as the most likely cleavers of the samples. Finally, the method displays the cleavage not only according to peptides, but also according to proteins, permitting evaluation of whether the cleavage pattern is general across all proteins, or specific to a subset. We illustrate the application of this method using milk hydrolysates, and show how it can rapidly identify the enzymes or enzyme combinations used in generating the peptides. The approach developed here will accelerate the identification of enzymes most likely to have been used in hydrolyzing a set of mass spectrometrically identified peptides derived from proteins. This has utility not only in understanding the results of mass spectrometry experiments, but also in choosing enzymes likely to yield similar cleavage patterns. EnzymePredictor can be found at http://bioware.ucd.ie/∼enzpred/Enzpred.php.

Protein disorder and short conserved motifs in disordered regions are enriched near the cytoplasmic side of single-pass transmembrane proteins.

Intracellular juxtamembrane regions of transmembrane proteins play pivotal roles in cell signalling, mediated by protein-protein interactions. Disordered protein regions, and short conserved motifs within them, are emerging as key determinants of many such interactions. Here, we investigated whether disorder and conserved motifs are enriched in the juxtamembrane area of human single-pass transmembrane proteins. Conserved motifs were defined as short disordered regions that were much more conserved than the adjacent disordered residues. Human single-pass proteins had higher mean disorder in their cytoplasmic segments than their extracellular parts. Some, but not all, of this effect reflected the shorter length of the cytoplasmic tail. A peak of cytoplasmic disorder was seen at around 30 residues from the membrane. We noted a significant increase in the incidence of conserved motifs within the disordered regions at the same location, even after correcting for the extent of disorder. We conclude that elevated disorder within the cytoplasmic tail of many transmembrane proteins is likely to be associated with enrichment for signalling interactions mediated by conserved short motifs.

Correlation of disorder between S. cerevisiae interacting proteins.

Protein disorder has been frequently associated with protein-protein interaction. However, our knowledge of how protein disorder evolves within a network is limited. It is expected that physically interacting proteins evolve in a coordinated manner. This has so far been shown in their evolutionary rate, and in their gene expression levels. Here we examine the percentage of predicted disorder residues within binary and complex interacting proteins (physical and functional interactions respectively) to investigate how the disorder of a protein relates to that of its interacting partners. We show that the level of disorder of interacting proteins are correlated, with a greater correlation seen among proteins that are co-members of the same complex, and a lesser correlation between proteins that are documented as binary interactors of each other. There is a striking variation among complexes not only in their disorder, but in the extent to which the proteins within the complex differ in their levels of disorder, with RNA processes and protein binding complexes showing more variation in the disorder of their proteins, whilst other complexes show very little variation in the overall disorder of their constituent proteins. There is likely to be a stronger selection for complex subunits to have similar disorder, than is seen for proteins involved in binary interactions. Thus, binary interactions may be more resilient to changes in disorder than are complex interactions. These results add a new dimension to the role of disorder in protein networks, and highlight the potential importance of maintaining similar disorder in the members of a complex.

Differences in the number of intrinsically disordered regions between yeast duplicated proteins, and their relationship with functional divergence.

BACKGROUND: Intrinsically disordered regions are enriched in short interaction motifs that play a critical role in many protein-protein interactions. Since new short interaction motifs may easily evolve, they have the potential to rapidly change protein interactions and cellular signaling. In this work we examined the dynamics of gain and loss of intrinsically disordered regions in duplicated proteins to inspect if changes after genome duplication can create functional divergence. For this purpose we used Saccharomyces cerevisiae and the outgroup species Lachancea kluyveri. PRINCIPAL FINDINGS: We find that genes duplicated as part of a genome duplication (ohnologs) are significantly more intrinsically disordered than singletons (p<2.2(e)-16, Wilcoxon), reflecting a preference for retaining intrinsically disordered proteins in duplicate. In addition, there have been marked changes in the extent of intrinsic disorder following duplication. A large number of duplicated genes have more intrinsic disorder than their L. kluyveri ortholog (29% for duplicates versus 25% for singletons) and an even greater number have less intrinsic disorder than the L. kluyveri ortholog (37% for duplicates versus 25% for singletons). Finally, we show that the number of physical interactions is significantly greater in the more intrinsically disordered ohnolog of a pair (p = 0.003, Wilcoxon). CONCLUSION: This work shows that intrinsic disorder gain and loss in a protein is a mechanism by which a genome can also diverge and innovate. The higher number of interactors for proteins that have gained intrinsic disorder compared with their duplicates may reflect the acquisition of new interaction partners or new functional roles.