Natural Protein Sequences Research Articles

FAD-BERT: Improved prediction of FAD binding sites using pre-training of deep bidirectional transformers

The electron transport chain is a series of protein complexes embedded in the process of cellular respiration, which is an important process to transfer electrons and other macromolecules throughout the cell. Identifying Flavin Adenine Dinucleotide (FAD) binding sites in the electron transport chain is vital since it helps biological researchers precisely understand how electrons are produced and are transported in cells. This study distills and analyzes the contextualized word embedding from pre-trained BERT models to explore similarities in natural language and protein sequences. Thereby, we propose a new approach based on Pre-training of Bidirectional Encoder Representations from Transformers (BERT), Position-specific Scoring Matrix profiles (PSSM), Amino Acid Index database (AAIndex) to predict FAD-binding sites from the transport proteins which are found in nature recently. Our proposed approach archives 85.14% accuracy and improves accuracy by 11%, with Matthew's correlation coefficient of 0.39 compared to the previous method on the same independent set. We also deploy a web server that identifies FAD-binding sites in electron transporters available for academics at http://140.138.155.216/fadbert/.

A protein sequence fitness function for identifying natural and nonnatural proteins.

The infinitesimally small sequence space naturally scouted in the millions of years of evolution suggests that the natural proteins are constrained by some functional prerequisites and should differ from randomly generated sequences. We have developed a protein sequence fitness scoring function that implements sequence and corresponding secondary structural information at tripeptide levels to differentiate natural and nonnatural proteins. The proposed fitness function is extensively validated on a dataset of about 210 000 natural and nonnatural protein sequences and benchmarked with existing methods for differentiating natural and nonnatural proteins. The high sensitivity, specificity, and percentage accuracy (0.81%, 0.95%, and 91% respectively) of the fitness function demonstrates its potential application for sampling the protein sequences with higher probability of mimicking natural proteins. Moreover, the four major classes of proteins (α proteins, β proteins, α/β proteins, and α + β proteins) are separately analyzed and β proteins are found to score slightly lower as compared to other classes. Further, an analysis of about 250 designed proteins (adopted from previously reported cases) helped to define the boundaries for sampling the ideal protein sequences. The protein sequence characterization aided by the proposed fitness function could facilitate the exploration of new perspectives in the design of novel functional proteins.

Prediction of Amphiphilic Cell-Penetrating Peptide Building Blocks from Protein-Derived Amino Acid Sequences for Engineering of Drug Delivery Nanoassemblies.

Amphiphilic molecules, forming self-assembled nanoarchitectures, are typically composed of hydrophobic and hydrophilic domains. Peptide amphiphiles can be designed from two, three, or four building blocks imparting novel structural and functional properties and affinities for interaction with cellular membranes or intracellular organelles. Here we present a combined numerical approach to design amphiphilic peptide scaffolds that are derived from the human nuclear Ki-67 protein. Ki-67 acts, like a biosurfactant, as a steric and electrostatic charge barrier against the collapse of mitotic chromosomes. The proposed predictive design of new Ki-67 protein-derived amphiphilic amino acid sequences exploits the computational outcomes of a set of web-accessible predictors, which are based on machine learning methods. The ensemble of such artificial intelligence algorithms, involving support vector machine (SVM), random forest (RF) classifiers, and neural networks (NN), enables the nanoengineering of a broad range of innovative peptide materials for therapeutic delivery in various applications. Amphiphilic cell-penetrating peptides (CPP), derived from natural protein sequences, may spontaneously form self-assembled nanocarriers characterized by enhanced cellular uptake. Thanks to their inherent low immunogenicity, they may enable the safe delivery of therapeutic molecules across the biological barriers.

Statistical physics of interacting proteins: Impact of dataset size and quality assessed in synthetic sequences.

Identifying protein-protein interactions is crucial for a systems-level understanding of the cell. Recently, algorithms based on inverse statistical physics, e.g., direct coupling analysis (DCA), have allowed to use evolutionarily related sequences to address two conceptually related inference tasks: finding pairs of interacting proteins and identifying pairs of residues which form contacts between interacting proteins. Here we address two underlying questions: How are the performances of both inference tasks related? How does performance depend on dataset size and the quality? To this end, we formalize both tasks using Ising models defined over stochastic block models, with individual blocks representing single proteins and interblock couplings protein-protein interactions; controlled synthetic sequence data are generated by Monte Carlo simulations. We show that DCA is able to address both inference tasks accurately when sufficiently large training sets of known interaction partners are available and that an iterative pairing algorithm allows to make predictions even without a training set. Noise in the training data deteriorates performance. In both tasks we find a quadratic scaling relating dataset quality and size that is consistent with noise adding in square-root fashion and signal adding linearly when increasing the dataset. This implies that it is generally good to incorporate more data even if their quality are imperfect, thereby shedding light on the empirically observed performance of DCA applied to natural protein sequences.

Natural vs. random protein sequences: the novel neural network approach based on time series analysis

We study the set of about 35000 primary structures of natural proteins of length more than 360 residues and the same size set generated via partial or total randomization at the amino acid level. Associated to every sequence composed of 20 amino acids, a time series is formed from hydropathy values of the first 360 residues. To measure the absolute deviations of hydropathy index on different time scales, the 24-dimensional vector of total log amplitudes is introduced. We describe then a configuration of the two-hidden layer neural network which is trained to solve the binary classification problem of natural and random sequences. A satisfactory distinguishing accuracy random/natural of $$88\%$$ is achieved.

Measuring the Physical Properties of Synthetic Cement Derived Barnacle Adhesive Nanomaterials from the Barnacle Amphibalanus amphitrite

Barnacles, a common biofouling organism, produce a micron-thick layer of long-lasting, proteinaceous, amyloid-like underwater adhesive. The barnacle triggers assembly and delivery of glue materials from a liquid, which set as a bulk adhesive gel in seawater environments. To accomplish this, barnacles use an organized co-block arrangement of sequence domains to dictate material formation, cohesive strength, and adhesive bonding properties of larger protein components. To understand physical properties of barnacle glue and construct a new class of underwater adhesive biomaterials, we have established a materials library from barnacle protein sequences for study. Derived from consensus barnacle cement sequences of Amphibalanus amphitrite natural glue proteins, we use eight synthetic barnacle cement peptides (BCPs), with patterned sequence motifs from the natural adhesive, for materials processing and testing. To understand how barnacle adhesive is fabricated, we looked closely at the BCP sequence to evaluate its function in delivery, polymerization, curing, and adhesion. Bulk gel materials, characterized by atomic force microscopy and profilometry, are shown to form dependent upon a BCP sequence pattern. The adhesive properties of assembled BCP gels were measured using adhesion testing of pull off (normal force) and push off (shear force). In shear force, patterned sequences behave differently demonstrating the structuring of fibrils enhances resistance to shear. Bulk BCP gels are shown to be pressure sensitive and have similar strength to natural barnacle glue. The alternating patterns identified suggest glue chemistries are displayed in the BCPs. Using BCPs, we link distinct peptide sequences to polymerization, recognition, formation of fibrils, and the physical properties of bulk gel materials. We offer a means to develop and test a protein library to establish the relationship between dry adhesion and biomaterial structure for insights into how natural barnacles adhere.

Natural and pathogenic protein sequence variation affecting prion-like domains within and across human proteomes

BackgroundImpaired proteostatic regulation of proteins with prion-like domains (PrLDs) is associated with a variety of human diseases including neurodegenerative disorders, myopathies, and certain forms of cancer. For many of these disorders, current models suggest a prion-like molecular mechanism of disease, whereby proteins aggregate and spread to neighboring cells in an infectious manner. The development of prion prediction algorithms has facilitated the large-scale identification of PrLDs among “reference” proteomes for various organisms. However, the degree to which intraspecies protein sequence diversity influences predicted prion propensity has not been systematically examined.ResultsHere, we explore protein sequence variation introduced at genetic, post-transcriptional, and post-translational levels, and its influence on predicted aggregation propensity for human PrLDs. We find that sequence variation is relatively common among PrLDs and in some cases can result in relatively large differences in predicted prion propensity. Sequence variation introduced at the post-transcriptional level (via alternative splicing) also commonly affects predicted aggregation propensity, often by direct inclusion or exclusion of a PrLD. Finally, analysis of a database of sequence variants associated with human disease reveals a number of mutations within PrLDs that are predicted to increase prion propensity.ConclusionsOur analyses expand the list of candidate human PrLDs, quantitatively estimate the effects of sequence variation on the aggregation propensity of PrLDs, and suggest the involvement of prion-like mechanisms in additional human diseases.

A thermo-responsive, self-assembling biointerface for on demand release of surface-immobilised proteins.

Dedicated chemistries for on-demand capture and release of biomolecules at the solid-liquid interface are required for applications in drug delivery, for the synthesis of switchable surfaces used in analytical devices and for the assembly of next-generation biomaterials with complex architectures and functions. Here we report the engineering of a binary self-assembling polypeptide system for reversible protein capture, immobilisation and controlled thermo-responsive release from a solid surface. The first element of the binary system is a universal protein substrate immobilised on a solid surface. This protein is bio-inspired by the neuronal SNAP25, which is the protein involved in the docking and fusion of synaptic vesicles to the synaptic membrane. The second element is an artificial chimeric protein engineered to include distinct domains from three different proteins: Syntaxin, VAMP and SNAP25. These native proteins constitute the machinery dedicated to vesicle trafficking in eukaryotes. We removed approximately 70% of native protein sequence from these proteins and constructed a protein chimera capable of high affinity interaction and self-assembly with immobilised substrate. The interaction of the two parts of the engineered protein complex is strong but fully-reversible and therefore the chimera can be recombinantly fused as a tag to a protein of interest, to allow spontaneous assembly and stimuli-sensitive release from the surface upon heating at a predetermined temperature. Two thermo-responsive tags are reported: the first presents remarkable thermal stability with melting temperature of the order of 80 °C; the second disassembles at a substantially lower temperature of about 45 °C. The latter is a promising candidate for remote-controlled localised delivery of therapeutic proteins, as physiologically tolerable local increase of temperatures in the 40-45 °C range can be achieved using magnetic fields, infra-red light or focused ultrasound. Importantly, these two novel polypeptides provide a broader blueprint for the engineering of future functional proteins with predictable folding and response to external stimuli.

A Review on the Recent Developments of Sequence-based Protein Feature Extraction Methods

Background:Proteins play a crucial role in life activities, such as catalyzing metabolic reactions, DNA replication, responding to stimuli, etc. Identification of protein structures and functions are critical for both basic research and applications. Because the traditional experiments for studying the structures and functions of proteins are expensive and time consuming, computational approaches are highly desired. In key for computational methods is how to efficiently extract the features from the protein sequences. During the last decade, many powerful feature extraction algorithms have been proposed, significantly promoting the development of the studies of protein structures and functions.Objective:To help the researchers to catch up the recent developments in this important field, in this study, an updated review is given, focusing on the sequence-based feature extractions of protein sequences.Method:These sequence-based features of proteins were grouped into three categories, including composition-based features, autocorrelation-based features and profile-based features. The detailed information of features in each group was introduced, and their advantages and disadvantages were discussed. Besides, some useful tools for generating these features will also be introduced.Results:Generally, autocorrelation-based features outperform composition-based features, and profile-based features outperform autocorrelation-based features. The reason is that profile-based features consider the evolutionary information, which is useful for identification of protein structures and functions. However, profile-based features are more time consuming, because the multiple sequence alignment process is required.Conclusion:In this study, some recently proposed sequence-based features were introduced and discussed, such as basic k-mers, PseAAC, auto-cross covariance, top-n-gram etc. These features did make great contributions to the developments of protein sequence analysis. Future studies can be focus on exploring the combinations of these features. Besides, techniques from other fields, such as signal processing, natural language process (NLP), image processing etc., would also contribute to this important field, because natural languages (such as English) and protein sequences share some similarities. Therefore, the proteins can be treated as documents, and the features, such as k-mers, top-n-grams, motifs, can be treated as the words in the languages. Techniques from these filed will give some new ideas and strategies for extracting the features from proteins.

Abstract A024: Identifying a T-cell receptor for immunotherapy against a leukemia-associated self-antigen in an allogeneic setting

Abstract Our aim was to identify a T-cell receptor (TCR) that recognizes a leukemia-associated self-antigen that is highly expressed in acute myeloid leukemia and that is restricted by HLA-A2 (expressed in approximately 50% of Caucasians) for future adoptive T-cell therapy. Tumor-associated self-antigens (self-TAA) make attractive targets for adoptive T-cell therapy in cancer. However, autologous T-cells are tolerant to self-TAA expressed on self-HLA if antigen expression is sufficiently high. In contrast, T-cells are not tolerant to self-antigens presented on non-self HLA. By identifying alloreactive T-cells reactive to self-peptide in complex with foreign HLA and subsequently cloning their TCR, it is possible to redirect patient T-cells against self-TAA. The advantage of targeting self-antigens is the potential application of TCR-engineered T-cells in all cancer patients with a certain cancer type and expressing the restricting HLA molecule. We identified a protein that is selectively expressed in normal myeloid cells and highly expressed by leukemic cells in patients with acute myeloid leukemia (AML). HLA-A2 negative CD8 T-cells from healthy donors were stimulated with HLA-A2 positive dendritic cells that were expressing the full-length target antigen. CD8 T-cells recognizing peptides from the target antigen in complex with HLA-A2 were subsequently identified and sorted using fluorescently labeled HLA-A2-peptide-multimers. T-cell clones obtained from sorted multimer-positive T-cells responded to HLA-A2 positive patient leukemia cells and cell lines expressing the antigen, whereas no response was seen to HLA-A2 positive antigen negative targeT-cells, unless loaded with the relevant peptide. Responses were measured as secretion of interferon gamma, expression of the T-cell activation marker CD137 or expression of the degranulation marker CD107. The TCR sequences were identified, and we next evaluated specificity and functionality of TCR-engineered cells. Indeed, TCR-transduced healthy donor T-cells were able to selectively kill HLA-A2 positive AML patient-derived leukemic cells as well as antigen positive cell lines in vitro. We also performed a comprehensive peptide scan to determine potential cross reactivity of the obtained TCR. For this scan, the amino acids (AA) in each position of the target peptide-epitope were individually replaced by every other natural amino acid (19 AA for each position x 9 postions = 171 peptides). In addition, potential responses to other variants of the 9mer target peptide based on the natural antigen protein sequence were evaluated (8mer, 10mers, 11mers and 12mers). Based on the results obtained with TCR-transduced T-cells (interferon gamma ELISA), candidate peptides naturally occurring in the proteome to which our TCR could cross-react were not identified. These data warrant further evaluation of the TCR for in vivo efficacy and specificity. Citation Format: Maxi-Lu Böschen, Weiwen Yang, Erlend Strønen, Johanna Olweus. Identifying a T-cell receptor for immunotherapy against a leukemia-associated self-antigen in an allogeneic setting [abstract]. In: Proceedings of the Fourth CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; Sept 30-Oct 3, 2018; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2019;7(2 Suppl):Abstract nr A024.

Food-Derived Bioactive Peptides in Human Health: Challenges and Opportunities.

Recent scientific evidence suggests that food proteins not only serve as nutrients, but can also modulate the body’s physiological functions. These physiological functions are primarily regulated by some peptides that are encrypted in the native protein sequences. These bioactive peptides can exert health beneficial properties and thus are considered as a lead compound for the development of nutraceuticals or functional foods. In the past few decades, a wide range of food-derived bioactive peptide sequences have been identified, with multiple health beneficial activities. However, the commercial application of these bioactive peptides has been delayed because of the absence of appropriate and scalable production methods, proper exploration of the mechanisms of action, high gastro-intestinal digestibility, variable absorption rate, and the lack of well-designed clinical trials to provide the substantial evidence for potential health claims. This review article discusses the current techniques, challenges of the current bioactive peptide production techniques, the oral use and gastrointestinal bioavailability of these food-derived bioactive peptides, and the overall regulatory environment.

Chemo- and Regioselective Lysine Modification on Native Proteins.

Site-selective chemical conjugation of synthetic molecules to proteins expands their functional and therapeutic capacity. Current protein modification methods, based on synthetic and biochemical technologies, can achieve site selectivity, but these techniques often require extensive sequence engineering or are restricted to the N- or C-terminus. Here we show the computer-assisted design of sulfonyl acrylate reagents for the modification of a single lysine residue on native protein sequences. This feature of the designed sulfonyl acrylates, together with the innate and subtle reactivity differences conferred by the unique local microenvironment surrounding each lysine, contribute to the observed regioselectivity of the reaction. Moreover, this site selectivity was predicted computationally, where the lysine with the lowest pKa was the kinetically favored residue at slightly basic pH. Chemoselectivity was also observed as the reagent reacted preferentially at lysine, even in those cases when other nucleophilic residues such as cysteine were present. The reaction is fast and proceeds using a single molar equivalent of the sulfonyl acrylate reagent under biocompatible conditions (37 °C, pH 8.0). This technology was demonstrated by the quantitative and irreversible modification of five different proteins including the clinically used therapeutic antibody Trastuzumab without prior sequence engineering. Importantly, their native secondary structure and functionality is retained after the modification. This regioselective lysine modification method allows for further bioconjugation through aza-Michael addition to the acrylate electrophile that is generated by spontaneous elimination of methanesulfinic acid upon lysine labeling. We showed that a protein–antibody conjugate bearing a site-specifically installed fluorophore at lysine could be used for selective imaging of apoptotic cells and detection of Her2+ cells, respectively. This simple, robust method does not require genetic engineering and may be generally used for accessing diverse, well-defined protein conjugates for basic biology and therapeutic studies.

Selection originating from protein stability/foldability: Relationships between protein folding free energy, sequence ensemble, and fitness

Assuming that mutation and fixation processes are reversible Markov processes, we prove that the equilibrium ensemble of sequences obeys a Boltzmann distribution with exp(4Nem(1−1/(2N))), where m is Malthusian fitness and Ne and N are effective and actual population sizes. On the other hand, the probability distribution of sequences with maximum entropy that satisfies a given amino acid composition at each site and a given pairwise amino acid frequency at each site pair is a Boltzmann distribution with exp(−ψN), where the evolutionary statistical energy ψN is represented as the sum of one body (h) (compositional) and pairwise (J) (covariational) interactions over all sites and site pairs. A protein folding theory based on the random energy model (REM) indicates that the equilibrium ensemble of natural protein sequences is well represented by a canonical ensemble characterized by exp(−ΔGND/kBTs) or by exp(−GN/kBTs) if an amino acid composition is kept constant, where ΔGND≡GN−GD,GN and GD are the native and denatured free energies, and Ts is the effective temperature representing the strength of selection pressure. Thus, 4Nem(1−1/(2N)),−ΔψND(≡−ψN+ψD), and −ΔGND/kBTs must be equivalent to each other. With h and J estimated by the DCA program, the changes (ΔψN) of ψN due to single nucleotide nonsynonymous substitutions are analyzed. The results indicate that the standard deviation of ΔGN(=kBTsΔψN) is approximately constant irrespective of protein families, and therefore can be used to estimate the relative value of Ts. Glass transition temperature Tg and ΔGND are estimated from estimated Ts and experimental melting temperature (Tm) for 14 protein domains. The estimates of ΔGND agree with their experimental values for 5 proteins, and those of Ts and Tg are all within a reasonable range. In addition, approximating the probability density function (PDF) of ΔψN by a log-normal distribution, PDFs of ΔψN and Ka/Ks, which is the ratio of nonsynonymous to synonymous substitution rate per site, in all and in fixed mutants are estimated. The equilibrium values of ψN, at which the average of Δψ in fixed mutants is equal to zero, well match ψN averaged over homologous sequences, confirming that the present methods for a fixation process of mutations and for the equilibrium ensemble of ψN give a consistent result with each other. The PDFs of Ka/Ks at equilibrium confirm that Ts negatively correlates with the amino acid substitution rate (the mean of Ka/Ks) of protein. Interestingly, stabilizing mutations are significantly fixed by positive selection, and balance with destabilizing mutations fixed by random drift, although most of them are removed from population. Supporting the nearly neutral theory, neutral selection is not significant even in fixed mutants.

Inferring repeat-protein energetics from evolutionary information.

Natural protein sequences contain a record of their history. A common constraint in a given protein family is the ability to fold to specific structures, and it has been shown possible to infer the main native ensemble by analyzing covariations in extant sequences. Still, many natural proteins that fold into the same structural topology show different stabilization energies, and these are often related to their physiological behavior. We propose a description for the energetic variation given by sequence modifications in repeat proteins, systems for which the overall problem is simplified by their inherent symmetry. We explicitly account for single amino acid and pair-wise interactions and treat higher order correlations with a single term. We show that the resulting evolutionary field can be interpreted with structural detail. We trace the variations in the energetic scores of natural proteins and relate them to their experimental characterization. The resulting energetic evolutionary field allows the prediction of the folding free energy change for several mutants, and can be used to generate synthetic sequences that are statistically indistinguishable from the natural counterparts.

Heterogeneous-Backbone Foldamer Mimics of Zinc Finger Tertiary Structure.

A variety of oligomeric backbones with compositions deviating from biomacromolecules can fold in defined ways. Termed "foldamers," these agents have diverse potential applications. A number of protein-inspired secondary structures (e.g., helices, sheets) have been produced from unnatural backbones, yet examples of tertiary folds combining several secondary structural elements in a single entity are rare. One promising strategy to address this challenge is the systematic backbone alteration of natural protein sequences, through which a subset of the native side chains is displayed on an unnatural building block to generate a heterogeneous backbone. A drawback to this approach is that substitution at more than one or two sites often comes at a significant energetic cost to fold stability. Here we report heterogeneous-backbone foldamers that mimic the zinc finger domain, a ubiquitous and biologically important metal-binding tertiary motif, and do so with a folded stability that is superior to the natural protein on which their design is based. A combination of UV-vis spectroscopy, isothermal titration calorimetry, and multidimensional NMR reveals that suitably designed oligomers with >20% modified backbones can form native-like tertiary folds with metal-binding environments identical to the prototype sequence (the third finger of specificity factor 1) and enhanced thermodynamic stability. These results expand the scope of heterogeneous-backbone foldamer design to a new tertiary structure class and show that judiciously applied backbone modification can be accompanied by improvement to fold stability.

Mutation effects predicted from sequence co-variation.

Many high-throughput experimental technologies have been developed to assess the effects of large numbers of mutations (variation) on phenotypes. However, designing functional assays for these methods is challenging, and systematic testing of all combinations is impossible, so robust methods to predict the effects of genetic variation are needed. Most prediction methods exploit evolutionary sequence conservation but do not consider the interdependencies of residues or bases. We present EVmutation, an unsupervised statistical method for predicting the effects of mutations that explicitly captures residue dependencies between positions. We validate EVmutation by comparing its predictions with outcomes of high-throughput mutagenesis experiments and measurements of human disease mutations and show that it outperforms methods that do not account for epistasis. EVmutation can be used to assess the quantitative effects of mutations in genes of any organism. We provide pre-computed predictions for ∼7,000 human proteins at http://evmutation.org/.

Development and Identification of a Novel Anti-HIV-1 Peptide Derived by Modification of the N-Terminal Domain of HIV-1 Integrase

The viral enzyme integrase (IN) is essential for the replication of human immunodeficiency virus type 1 (HIV-1) and represents an important target for the development of new antiretroviral drugs. In this study, we focused on the N-terminal domain (NTD), which is mainly involved into protein oligomerization process, for the development and synthesis of a library of overlapping peptide sequences, with specific length and specific offset covering the entire native protein sequence NTD IN 1–50. The most potent fragment, VVAKEIVAH (peptide 18), which includes a His residue instead of the natural Ser at position 39, inhibits the HIV-1 IN activity with an IC50 value of 4.5 μM. Amino acid substitution analysis on this peptide revealed essential residues for activity and allowed us to identify two nonapeptides (peptides 24 and 25), that show a potency of inhibition similar to the one of peptide 18. Interestingly, peptide 18 does not interfere with the dynamic interplay between IN subunits, while peptides 24 and 25 modulated these interactions in different manners. In fact, peptide 24 inhibited the IN-IN dimerization, while peptide 25 promoted IN multimerization, with IC50 values of 32 and 4.8 μM, respectively. In addition, peptide 25 has shown to have selective anti-infective cell activity for HIV-1. These results confirmed peptide 25 as a hit for further development of new chemotherapeutic agents against HIV-1.

Folding and Stabilization of Native-Sequence-Reversed Proteins.

Though the problem of sequence-reversed protein folding is largely unexplored, one might speculate that reversed native protein sequences should be significantly more foldable than purely random heteropolymer sequences. In this article, we investigate how the reverse-sequences of native proteins might fold by examining a series of small proteins of increasing structural complexity (α-helix, β-hairpin, α-helix bundle, and α/β-protein). Employing a tandem protein structure prediction algorithmic and molecular dynamics simulation approach, we find that the ability of reverse sequences to adopt native-like folds is strongly influenced by protein size and the flexibility of the native hydrophobic core. For β-hairpins with reverse-sequences that fail to fold, we employ a simple mutational strategy for guiding stable hairpin formation that involves the insertion of amino acids into the β-turn region. This systematic look at reverse sequence duality sheds new light on the problem of protein sequence-structure mapping and may serve to inspire new protein design and protein structure prediction protocols.

Structural hot spots for the solubility of globular proteins.

Natural selection shapes protein solubility to physiological requirements and recombinant applications that require higher protein concentrations are often problematic. This raises the question whether the solubility of natural protein sequences can be improved. We here show an anti-correlation between the number of aggregation prone regions (APRs) in a protein sequence and its solubility, suggesting that mutational suppression of APRs provides a simple strategy to increase protein solubility. We show that mutations at specific positions within a protein structure can act as APR suppressors without affecting protein stability. These hot spots for protein solubility are both structure and sequence dependent but can be computationally predicted. We demonstrate this by reducing the aggregation of human α-galactosidase and protective antigen of Bacillus anthracis through mutation. Our results indicate that many proteins possess hot spots allowing to adapt protein solubility independently of structure and function.

Natural protein sequences are more intrinsically disordered than random sequences.

Most natural protein sequences have resulted from millions or even billions of years of evolution. How they differ from random sequences is not fully understood. Previous computational and experimental studies of random proteins generated from noncoding regions yielded inclusive results due to species-dependent codon biases and GC contents. Here, we approach this problem by investigating 10,000 sequences randomized at the amino acid level. Using well-established predictors for protein intrinsic disorder, we found that natural sequences have more long disordered regions than random sequences, even when random and natural sequences have the same overall composition of amino acid residues. We also showed that random sequences are as structured as natural sequences according to contents and length distributions of predicted secondary structure, although the structures from random sequences may be in a molten globular-like state, according to molecular dynamics simulations. The bias of natural sequences toward more intrinsic disorder suggests that natural sequences are created and evolved to avoid protein aggregation and increase functional diversity.