Random Amino Acid Sequences Research Articles

Structure-guided in vitro evolution of nanobodies targeting new viral variants.

A major challenge in antiviral antibody therapy is keeping up with the rapid evolution of viruses. Our research shows that nanobodies - single-domain antibodies derived from camelids - can be rapidly re-engineered to combat new viral strains through structure-guided in vitro evolution. Specifically, for viral mutations occurring at nanobody-binding sites, we introduce randomized amino acid sequences into nanobody residues near these mutations. We then select nanobody variants that effectively bind to the mutated viral target from a phage display library. As a proof of concept, we used this approach to adapt Nanosota-3, a nanobody originally identified to target the receptor-binding domain (RBD) of early Omicron subvariants, making it highly effective against recent Omicron subvariants. Remarkably, this adaptation process can be completed in less than two weeks, allowing drug development to keep pace with viral evolution and provide timely protection to humans.

Monocarboxylate transporter-1 (MCT-1) inhibitors screened from autodisplayed FV-antibody library

Monocarboxylate transporter-1 (MCT-1) inhibitors were screened from the Fv-antibody library, which contained complementary determining region 3 with randomized amino acid sequences (11 residues) through site-directed mutagenesis. Fv-antibodies against MCT-1 were screened from the autodisplayed Fv-antibody library. Two clones were screened, and the binding affinity (KD) against MCT-1 was estimated using flow cytometry. The screened Fv-antibodies were expressed as soluble fusion proteins (Fv-1 and Fv-2) and the KD for MCT-1 was estimated using the SPR biosensor. The inhibitory activity of the expressed Fv-antibodies was observed in HEK293T and Jurkat cell lines by measuring intracellular pH and lactate accumulation. The level of cell viability in HEK293T and Jurkat cell lines was decreased by the inhibitory activity of the expressed Fv-antibodies. The binding properties of the Fv-antibodies to MCT-1 were analyzed using molecular docking simulations. Overall, the results showed that the screened Fv-antibodies against MCT-1 from the Fv-antibody library had high binding affinity and inhibitory activity against MCT-1, which could be used as potential therapeutic drug candidates for the MCT-1 inhibitor.

When Data Are Lacking: Physics-Based Inverse Design of Biopolymers Interacting with Complex, Fluid Phases.

Biomolecular research traditionally revolves around comprehending the mechanisms through which peptides or proteins facilitate specific functions, often driven by their relevance to clinical ailments. This conventional approach assumes that unraveling mechanisms is a prerequisite for wielding control over functionality, which stands as the ultimate research goal. However, an alternative perspective emerges from physics-based inverse design, shifting the focus from mechanisms to the direct acquisition of functional control strategies. By embracing this methodology, we can uncover solutions that might not have direct parallels in natural systems, yet yield crucial insights into the isolated molecular elements dictating functionality. This provides a distinctive comprehension of the underlying mechanisms.In this context, we elucidate how physics-based inverse design, facilitated by evolutionary algorithms and coarse-grained molecular simulations, charts a promising course for innovating the reverse engineering of biopolymers interacting with intricate fluid phases such as lipid membranes and liquid protein phases. We introduce evolutionary molecular dynamics (Evo-MD) simulations, an approach that merges evolutionary algorithms with the Martini coarse-grained force field. This method directs the evolutionary process from random amino acid sequences toward peptides interacting with complex fluid phases such as biological lipid membranes, offering significant promises in the development of peptide-based sensors and drugs. This approach can be tailored to recognize or selectively target specific attributes such as membrane curvature, lipid composition, membrane phase (e.g., lipid rafts), and protein fluid phases. Although the resulting optimal solutions may not perfectly align with biological norms, physics-based inverse design excels at isolating relevant physicochemical principles and thermodynamic driving forces governing optimal biopolymer interaction within complex fluidic environments. In addition, we expound upon how physics-based evolution using the Evo-MD approach can be harnessed to extract the evolutionary optimization fingerprints of protein-lipid interactions from native proteins. Finally, we outline how such an approach is uniquely able to generate strategic training data for predictive neural network models that cover the whole relevant physicochemical domain. Exploring challenges, we address key considerations such as choosing a fitting fitness function to delineate the desired functionality. Additionally, we scrutinize assumptions tied to system setup, the targeted protein structure, and limitations posed by the utilized (coarse-grained) force fields and explore potential strategies for guiding evolution with limited experimental data. This discourse encapsulates the potential and remaining obstacles of physics-based inverse design, paving the way for an exciting frontier in biomolecular research.

Selection of a de novo gene that can promote survival of Escherichiacoli by modulating protein homeostasis pathways

Cellular novelty can emerge when non-functional loci become functional genes in a process termed de novo gene birth. But how proteins with random amino acid sequences beneficially integrate into existing cellular pathways remains poorly understood. We screened ~108 genes, generated from random nucleotide sequences and devoid of homology to natural genes, for their ability to rescue growth arrest of Escherichia coli cells producing the ribonuclease toxin MazF. We identified ~2,000 genes that could promote growth, probably by reducing transcription from the promoter driving toxin expression. Additionally, one random protein, named Random antitoxin of MazF (RamF), modulated protein homeostasis by interacting with chaperones, leading to MazF proteolysis and a consequent loss of its toxicity. Finally, we demonstrate that random proteins can improve during evolution by identifying beneficial mutations that turned RamF into a more efficient inhibitor. Our work provides a mechanistic basis for how de novo gene birth can produce functional proteins that effectively benefit cells evolving under stress.

Electrochemical aptasensors for pathogenic detection toward point-of-care diagnostics.

A biosensor system refers to a biomedical device, which detects biological, chemical, or biochemical components by converting those signals to an electrical signal by utilizing and uniting physical or chemical transducer with biorecognition elements. An electrochemical biosensor is generally based on the reaction of either production or consumption of electrons under a three-electrode system. Biosensor systems are exploited in a wide range of areas, such as medicine, agriculture, husbandry, food, industry, environment protection, quality control, waste disposal, and the military. Pathogenic infections are the third leading cause of death worldwide after cardiovascular diseases and cancer. Therefore, there is an urgent need for effective diagnostic tools to control food, water, and soil contamination result in protecting human life and health. Aptamers are peptide or oligonucleotide-based molecules that show very high affinity to their targets that are produced from large pools of random amino acid or oligonucleotide sequences. Generally, aptamers have been utilized for fundamental sciences and clinical implementations for their target-specific affinity and have been intensely exploited for different kinds of biosensor applications for approximately 30 years. The convergence of aptamers with biosensor systems enabled the construction of voltammetric, amperometric, and impedimetric biosensors for the detection of specific pathogens. In this review, electrochemical aptamer biosensors were evaluated by discussing the definition, types, and production techniques of aptamers, the advantages of aptamers as a biological recognition element against their alternatives, and a wide range of aptasensor examples from literature in the detection of specific pathogens.

Evaluating plasma antibody repertoires in breast cancer patients with progesterone receptor positive and negative tumors

Abstract Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer death in females worldwide. Early cancer detection and tumors molecular characterization are the key measures for effective therapy. Molecular changes associated with tumor development are detected by the immune system and reflected with specific antibody production. We used microarrays containing 120K of peptides with random amino acid sequences to analyze the repertoire of circulating antibodies in the blood plasma of patients with early stages of breast cancer (BC). Previously we have shown that healthy donors and BC patients have distinct circulating antibody repertoires. Further analysis reviled that BC patients with various progesterone receptor (PR) expression levels also have circulating antibodies reacting with different peptides (mimotopes) on microarrays. In this study we selected 61 peptides from 120K peptides presented on microarrays that show statistically significant difference for the interaction with the circulating antibodies of blood plasma from PR+ and PR- BC patients. Peptides interacting with PR+ and PR- patients plasma have common amino acid motifs suggesting that patients with tumors expressing different PR levels mount specific immune response reflecting molecular changes associated with various tumor subtype. Therefore, the panel of peptides that specifically interact with the immunoglobulins of patients with different PR expression can be used for the development of non-invasive test systems. Thus, peptide microarrays can be used to create a new tool for studying the repertoire of circulating antibodies for different diseases evaluation. Supported by grant FZMW-2020-0007. Supported by grants from the Ministry of Science and Higher Education of the Russian Federation (FZMW-2020-0007)

The interacting rotifer-biopolymers are anti- and disaggregating agents for human-type beta-amyloid in vitro

Neurodegeneration-related human-type beta-amyloid 1-42 aggregates (H-Aβ) are one of the biochemical markers and executive molecules in Alzheimer's disease. The exogenic rotifer-specific biopolymer, namely Rotimer, has a protective effect against H-Aβ toxicity on Euchlanis dilatata and Lecane bulla monogonant rotifers. Due to the external particle-dependent secreting activity of these animals, this natural exudate exists in a bound form on the surface of epoxy-metal beads, named as Rotimer Inductor Conglomerate (RIC). In this current work the experiential in vitro molecular interactions between Rotimer and Aβs are presented. The RIC form was uniformly used against H-Aβ aggregation processes in stagogram- and fluorescent-based experiments. These well-known cell-toxic aggregates stably and quickly (only taking a few minutes) bind to RIC. The epoxy beads (as carriers) alone or the scrambled version of H-Aβ (with random amino acid sequence) were the ineffective and inactive negative controls of this experimental system. The RIC has significant interacting, anti-aggregating and disaggregating effects on H-Aβ. To detect these experiments, Bis-ANS and Thioflavin T were applied during amyloid binding, two aggregation-specific functional fluorescent dyes with different molecular characteristics. This newly described empirical interaction of Rotimer with H-Aβ is a potential starting point and source of innovation concerning targeted human- and pharmaceutical applications.

De novo protein design by deep network hallucination.

There has been considerable recent progress in protein structure prediction using deep neural networks to predict inter-residue distances from amino acid sequences1-3. Here we investigate whether the information captured by such networks is sufficiently rich to generate new folded proteins with sequences unrelated to those of the naturally occurring proteins used in training the models. We generate random amino acid sequences, and input them into the trRosetta structure prediction network to predict starting residue-residue distance maps, which, as expected, are quite featureless. We then carry out Monte Carlo sampling in amino acid sequence space, optimizing the contrast (Kullback-Leibler divergence) between the inter-residuedistance distributions predicted by the network and background distributions averaged over all proteins. Optimization from different random starting points resulted in novel proteins spanning a wide range of sequences and predicted structures. We obtained synthetic genes encoding 129 of the network-'hallucinated' sequences, and expressed and purified the proteins in Escherichia coli; 27 of the proteins yielded monodisperse species with circular dichroism spectra consistent with the hallucinated structures. We determined the three-dimensionalstructures of three of the hallucinated proteins, two by X-ray crystallography and one by NMR, and these closely matched the hallucinated models. Thus, deep networks trained to predict native protein structures from their sequences can be inverted to design new proteins, and such networks and methods should contribute alongside traditional physics-based models to the de novo design of proteins with new functions.

Comprehensive Analysis of Intrinsically Disordered Regions to Elucidate Their Physical Properties using a Modified Parameter

Intrinsically Disordered Regions (IDRs) in proteins are often necessary for modulating intracellular liquid-liquid phase separation (LLPS). The recent study by Martin et al. [Science 367(6478):694-699 (2020)] has shown that the valence and patterning of aromatic residues in IDRs can promote LLPS. In the report, they used a parameter Ωaro to quantify patterning of aromatic residues, regarded as “stickers”. However, because Ωaro is sensitive to the amino acid composition of IDRs, only Ωaro is not appropriate to quantify the patterning of stickers in IDRs. In order to compare the sticker patterning across various IDRs, the parameter need to be independent of compositions of their sequences. Parameters, which are not affected by compositional bias, are useful for the sequence-based comprehensive analysis of physical property of proteins involved in LLPS.In this study, to solve this problem, we developed a modified patterning parameter using randomized amino acid sequences in statistical analysis. In more detail, we calculated the variability of the local sticker content for each randomized sequence. By learning dependencies on sequence lengths and sticker contents, we could obtain the value of parameter that represents the patterning of stickers with a value of 0-1, independent of the composition of IDRs. We calculated values of the parameter for the IDRs that have been reported to drive LLPS and their orthologous sequences to analyze their tendency of physical property and conservation among orthologous sequences. Finally, we performed molecular dynamics simulations to link the parameters to physical properties of IDRs.

Cofactors as Molecular Fossils To Trace the Origin and Evolution of Proteins.

Due to their early origin and extreme conservation, cofactors are valuable molecular fossils for tracing the origin and evolution of proteins. First, as the order of protein folds binding with cofactors roughly coincides with protein-fold chronology, cofactors are considered to have facilitated the origin of primitive proteins by selecting them from pools of random amino acid sequences. Second, in the subsequent evolution of proteins, cofactors still played an important role. More interestingly, as metallic cofactors evolved with geochemical variations, some geochemical events left imprints in the chronology of protein architecture; this provides further evidence supporting the coevolution of biochemistry and geochemistry. In this paper, we attempt to review the molecular fossils used in tracing the origin and evolution of proteins, with a special focus on cofactors.

REPERTOIRE OF CIRCULATING ANTIBODIES CORRESPONDING TO MOLECULAR SUBTYPES OF BREAST CANCER

Abstract Changes in the repertoire of circulating antibodies is a promising biomarker for various diseases. We have developed microarrays containing more than thousands of peptides with random amino acid sequences that can be used to analyze the entire repertoire of circulating antibodies. Using these peptide microarrays, we have shown that in the blood of patients diagnosed with breast cancer (BC) antibodies interacting with certain peptides are found. Moreover, circulating antibodies of patients with different molecular subtypes of BC (luminal A, luminal B and Basal-like) interact with different, but overlapping, panels of peptides. We have selected 634 peptides from 120K peptides presented on microarrays that specifically interact with circulating antibodies of blood plasma from patients with various subtypes of BC. This panel of peptides that specifically interact with the blood plasma immunoglobulins of patients with various molecular BC subtypes can be used for the development of non-invasive diagnostic test systems. Microchips containing thousands of peptides can be used as a tool for molecular screening populations at risk for various diseases. Thus, peptide microarrays can be used to create a new tool for studying the repertoire of circulating antibodies for different diseases evaluation, as well as for studying and evaluating protein-protein interactions.

Bioinformatics Toxicity Assessment of RB Protein from Transgenic Potato Resistant to Phytophthora infestans

The genetically engineered potato cultivar Katahdin SP951 is resistant to late blight disease, which is caused by Phytophthora infestans . The biosafety and food safety of this cultivar should be assessed prior to its commercialization. Toxicity is one of the parameters tested in the food safety evaluation of transgenics. Toxicity testing includes the bioinformatics analysis of the homology of the RB protein of Katahdin SP951 with known proteins. Therefore, this study aimed to perform the bioinformatics analysis of Katahdin SP951 RB protein against homologous toxin proteins. Bioinformatics analysis was conducted by first translating the RB gene into an amino acid sequence by using the Emboss Transeq program from the EMBL-EBI website. The Shuffle Protein Program was then applied to obtain the randomized amino acid sequences of the RB protein. The Basic Local Alignment Search Tool (BLAST) Protein search program was utilized to identify proteins with high similarity and homology. Moreover, BLAST Suite-2 was used to analyze the homology between two or more sequence alignments. Results showed high homology between the sequences of the RB protein and those of known resistance ( R ) proteins with an E value of less than 0.22. This result may be attributed to the presence of numerous R proteins in plants. Further analysis indicated that the sequence of the RB protein had extremely nonsignificant homology with sequences of proteins known to be toxic in the Entrez protein database of National Center for Biotechnology Information. Therefore, the RB protein is nontoxic.

In Silico Engineering of Synthetic Binding Proteins from Random Amino Acid Sequences

SummarySynthetic proteins with high affinity and selectivity for a protein target can be used as research tools, biomarkers, and pharmacological agents, but few methods exist to design such proteins de novo. To this end, the In-Silico Protein Synthesizer (InSiPS) was developed to design synthetic binding proteins (SBPs) that bind pre-determined targets while minimizing off-target interactions. InSiPS is a genetic algorithm that refines a pool of random sequences over hundreds of generations of mutation and selection to produce SBPs with pre-specified binding characteristics. As a proof of concept, we design SBPs against three yeast proteins and demonstrate binding and functional inhibition of two of three targets in vivo. Peptide SPOT arrays confirm binding sites, and a permutation array demonstrates target specificity. Our foundational approach will support the field of de novo design of small binding polypeptide motifs and has robust applicability while offering potential advantages over the limited number of techniques currently available.

The Relation between α-Helical Conformation and Amyloidogenicity

Amyloid fibrils are stable aggregates of misfolded proteins and polypeptides that are insoluble and resistant to protease activity. Abnormal formation of amyloid fibrils in vivo may lead to neurodegenerative disorders and other systemic amyloidosis, such as Alzheimer’s, Parkinson’s, and atherosclerosis. Because of their clinical importance, amyloids are under intense scientific research. It is believed that short polypeptide segments within proteins are responsible for the transformation of correctly folded proteins into parts of larger amyloid fibrils and that this transition is modulated by environmental factors, such as pH, salt concentration, interaction with the cell membrane, and interaction with metal ions. Most studies on amyloids focus on the amyloidogenic sequences. The focus of this study is on the structure of the amyloidogenic α-helical segments because the α-helical secondary structure has been recognized to be a key player in different stages of the amyloidogenesis process. We have previously shown that the α-helical conformation may be expressed by two parameters (θ and ρ) that form orthogonal coordinates based on the Ramachandran dihedrals (φ and ψ) and provide an illuminating interpretation of the α-helical conformation. By performing statistical analysis on α-helical conformations found in the Protein Data Bank, an apparent relation between α-helical conformation, as expressed by θ and ρ, and amyloidogenicity is revealed. Remarkably, random amino acid sequences, whose helical structures were obtained from the most probable dihedral angles, revealed the same dependency of amyloidogenicity, suggesting the importance of α-helical structure as opposed to sequence.

Antibody repertoire analysis in sera of breast cancer patients using a random peptide microarray differentiates cases from controls with high specificity and sensitivity

Abstract Breast cancer (BC) is the most common malignancy and the second most common cause of cancer-related mortality in women. Early detection of BC using different biomarkers is a key factor for successful BC therapy. Many studies have shown that serum antibodies (Ab) against tumor-associated antigens (TAAs) could be used as potential biomarkers in the detection of BC. However in some cases the most of informative TAAs for circulating Ab detection, at early stages of BC, might be unknown. Even if informative Ab targets are currently unknown it is possible to analyze Ab repertoire (immunosignature) using random peptide arrays. In the present study, we probe a custom 330,000 random 12-mer peptide microarray with sera from 42 BC cases and 41 controls to develop a diagnostic signature. We determined that differentiation of BC cases from controls with high sensitivity and specificity requires a panel of at least 100–150 peptides. Basic Local Alignment Search Tool (BLAST) searches suggest that the identified peptides have no similarity with known TAAs. However, despite the random nature of the amino acid sequence that were used in microchip design, common amino acid motif is present in the groups of peptides that react with sera Ab from patients with BC but not healthy control. Thus the evaluation of Ab repertoire (immunosignature) using microarray containing peptides with random amino acid sequences has a potential value in the early diagnosis of cancer.

Bioinformatic screening and detection of allergen cross-reactive IgE-binding epitopes.

Protein allergens can be related by cross‐reactivity. Allergens that share relevant sequence can cross‐react, those lacking sufficient similarity in their IgE antibody‐binding epitopes do not cross‐react. Cross‐reactivity is based on shared epitopes that is based on shared sequence and higher level structure (charge and shape). Epitopes are important in predicting cross‐reactivity potential and may provide the potential to establish criteria that identify homology among allergens. Selected allergen's IgE‐binding epitope sequences were used to determine how the FASTA algorithm could be used to identify a threshold of significance. A statistical measure (expectation value, E‐value) was used to identify a threshold specific to identifying cross‐reactivity potential. Peanut Ara h 1 and Ara h 2, shrimp tropomyosin Pen a 1, and birch tree pollen allergen, Bet v 1 were sources of known epitopes. Each epitope or set of epitopes was inserted into random amino acid sequence to create hypothetical proteins used as queries to an allergen database. Alignments with allergens were noted for the ability to match the epitope's source allergen as well as any cross‐reactive or other homologous allergens. A FASTA expectation value range (1 × 10−5–1 × 10−6) was identified that could act as a threshold to help identify cross‐reactivity potential.

Natural vs. random protein sequences: Discovering combinatorics properties on amino acid words

Casual mutations and natural selection have driven the evolution of protein amino acid sequences that we observe at present in nature. The question about which is the dominant force of proteins evolution is still lacking of an unambiguous answer. Casual mutations tend to randomize protein sequences while, in order to have the correct functionality, one expects that selection mechanisms impose rigid constraints on amino acid sequences. Moreover, one also has to consider that the space of all possible amino acid sequences is so astonishingly large that it could be reasonable to have a well tuned amino acid sequence indistinguishable from a random one.In order to study the possibility to discriminate between random and natural amino acid sequences, we introduce different measures of association between pairs of amino acids in a sequence, and apply them to a dataset of 1047 natural protein sequences and 10,470 random sequences, carefully generated in order to preserve the relative length and amino acid distribution of the natural proteins. We analyze the multidimensional measures with machine learning techniques and show that, to a reasonable extent, natural protein sequences can be differentiated from random ones.

Adaptive Assembly: Maximizing the Potential of a Given Functional Peptide with a Tailor-Made Protein Scaffold

Protein engineering that exploits known functional peptides holds great promise for generating novel functional proteins. Here we propose a combinatorial approach, termed adaptive assembly, which provides a tailor-made protein scaffold for a given functional peptide. A combinatorial library was designed to create a tailor-made scaffold, which was generated from β hairpins derived from a 10-residue minimal protein "chignolin" and randomized amino acid sequences. We applied adaptive assembly to a peptide with low affinity for the Fc region of human immunoglobulin G, generating a 54-residue protein AF.p17 with a 40,600-fold enhanced affinity. The crystal structure of AF.p17 complexed with the Fc region revealed that the scaffold fixed the active conformation with a unique structure composed of a short α helix, β hairpins, and a loop-like structure. Adaptive assembly can take full advantage of known peptides as assets for generating novel functional proteins.

Structural protein reorganization and fold emergence investigated through amino acid sequence permutations.

Correlation between random amino acid sequences and protein folds suggests that proteins autonomously evolved the most stable folds, with stability and function evolving subsequently, suggesting the existence of common protein ancestors from which all modern proteins evolved. To test this hypothesis, we shuffled the sequences of 10 natural proteins and obtained 40 different and apparently unrelated folds. Our results suggest that shuffled sequences are sufficiently stable and may act as a basis to evolve functional proteins. The common secondary structure of modern proteins is well represented by a small set of permuted sequences, which also show the emergence of intrinsic disorder and aggregation-prone stretches of the polypeptide chain.

A Nonadaptive Origin of a Beneficial Trait: In Silico Selection for Free Energy of Folding Leads to the Neutral Emergence of Mutational Robustness in Single Domain Proteins

Proteins are regarded as being robust to the deleterious effects of mutations. Here, the neutral emergence of mutational robustness in a population of single domain proteins is explored using computer simulations. A pairwise contact model was used to calculate the ΔG of folding (ΔG folding) using the three dimensional protein structure of leech eglin C. A random amino acid sequence with low mutational robustness, defined as the average ΔΔG resulting from a point mutation (ΔΔG average), was threaded onto the structure. A population of 1,000 threaded sequences was evolved under selection for stability, using an upper and lower energy threshold. Under these conditions, mutational robustness increased over time in the most common sequence in the population. In contrast, when the wild type sequence was used it did not show an increase in robustness. This implies that the emergence of mutational robustness is sequence specific and that wild type sequences may be close to maximal robustness. In addition, an inverse relationship between ∆∆G average and protein stability is shown, resulting partly from a larger average effect of point mutations in more stable proteins. The emergence of mutational robustness was also observed in the Escherichia coli colE1 Rop and human CD59 proteins, implying that the property may be common in single domain proteins under certain simulation conditions. The results indicate that at least a portion of mutational robustness in small globular proteins might have arisen by a process of neutral emergence, and could be an example of a beneficial trait that has not been directly selected for, termed a "pseudaptation."