Amino Acid Sequence Function Research Articles

Physics-driven coarse-grained model for biomolecular phase separation with near-quantitative accuracy

In recent years, various physics- and data-driven sequence-dependent protein coarse-grained models have been developed to study biomolecular liquid—liquid phase separation (LLPS) and elucidate the dominant physicochemical driving forces. Here, we present Mpipi, a multiscale coarse-grained model that describes almost quantitatively the change in protein critical temperatures as a function of amino-acid sequence. The model is parameterised from both atomistic simulations and bioinformatics data and accounts for the dominant role of π–π and hybrid cation–π/π–π interactions and the much stronger attractive contacts established by arginines than lysines.

Physics-driven coarse-grained model for biomolecular phase separation with near-quantitative accuracy.

Various physics- and data-driven sequence-dependent protein coarse-grained models have been developed to study biomolecular phase separation and elucidate the dominant physicochemical driving forces. Here, we present Mpipi, a multiscale coarse-grained model that describes almost quantitatively the change in protein critical temperatures as a function of amino-acid sequence. The model is parameterised from both atomistic simulations and bioinformatics data and accounts for the dominant role of π-π and hybrid cation-π/π-π interactions and the much stronger attractive contacts established by arginines than lysines. We provide a comprehensive set of benchmarks for Mpipi and seven other residue-level coarse-grained models against experimental radii of gyration and quantitative in-vitro phase diagrams; Mpipi predictions agree well with experiment on both fronts. Moreover, it can account for protein-RNA interactions, correctly predicts the multiphase behaviour of a charge-matched poly-arginine/poly-lysine/RNA system, and recapitulates experimental LLPS trends for sequence mutations on FUS, DDX4 and LAF-1 proteins.

Taxonomic bias in AMP prediction of invertebrate peptides

Invertebrate antimicrobial peptides (AMPs) are at the forefront in the search for agents of therapeutic utility against multi-resistant microbial pathogens, and in recent years substantial advances took place in the in silico prediction of antimicrobial function of amino acid sequences. A yet neglected aspect is taxonomic bias in the performance of these tools. Owing to differences in the prediction algorithms and used training data sets between tools, and phylogenetic differences in sequence diversity, physicochemical properties and evolved biological functions of AMPs between taxa, notable discrepancies may exist in performance between the currently available prediction tools. Here we tested if there is taxonomic bias in the prediction power in 10 tools with a total of 20 prediction algorithms in 19 invertebrate taxa, using a data set containing 1525 AMP and 3050 non-AMP sequences. We found that most of the tools exhibited considerable variation in performance between tested invertebrate groups. Based on the per-taxa performances and on the variation in performances across taxa we provide guidance in choosing the best-performing prediction tool for all assessed taxa, by listing the highest scoring tool for each of them.

Inferring Protein Sequence-Function Relationships with Large-Scale Positive-Unlabeled Learning.

Machine learning can infer how protein sequence maps to function without requiring a detailed understanding of the underlying physical or biological mechanisms. It is challenging to apply existing supervised learning frameworks to large-scale experimental data generated by deep mutational scanning (DMS) and related methods. DMS data often contain high-dimensional and correlated sequence variables, experimental sampling error and bias, and the presence of missing data. Notably, most DMS data do not contain examples of negative sequences, making it challenging to directly estimate how sequence affects function. Here, we develop a positive-unlabeled (PU) learning framework to infer sequence-function relationships from large-scale DMS data. Our PU learning method displays excellent predictive performance across ten large-scale sequence-function datasets, representing proteins of different folds, functions, and library types. The estimated parameters pinpoint key residues that dictate protein structure and function. Finally, we apply our statistical sequence-function model to design highly stabilized enzymes.

Translate gene sequence into gene ontology terms based on statistical machine translation

This paper presents a novel method to predict the functions of amino acid sequences, based on statistical machine translation programs. To build the translation model, we use the “parallel corpus” concept. For instance, an English sentence “I love apples” and its corresponding French sentence “j’adore les pommes” are examples of a parallel corpus. Here we regard an amino acid sequence like “MTMDKSELVQKA” as one language, and treat its functional description as “0005737 0006605 0019904 (Gene Ontology terms)” as a sentence of another language. We select amino acid sequences and their corresponding functional descriptions in Gene Ontology terms to build the parallel corpus. Then we use a phrase-based translation model to build the “amino acid sequence” to “protein function” translation model. The Bilingual Evaluation Understudy (BLEU) score, an algorithm for measuring the quality of machine-translated text, of the proposed method reaches about 0.6 when neglecting the order of Gene Ontology words. Although its functional prediction performance is still not as accurate as search-based methods, it was able to give the function of amino acid sequences directly and was more efficient.

From HIV protein sequences to viral fitness landscapes: a new paradigm for in silico vaccine design

Background An inexpensive prophylactic vaccine offers the best hope to curb the HIV/AIDS epidemic gripping sub-Saharan Africa. Systematic means to guide the design of an effective immunogen for this, and other, infectious diseases are not available. What is required is a method to chart the peaks and valleys of viral fitness as a function of amino acid sequence. An efficacious vaccine would eject the virus from the high fitness peaks, and drive it into the valleys where its compromised fitness impairs its ability to replicate and inflict damage to the host. Methods Appealing to spin glass models in statistical physics, we present a novel approach to translate viral sequence databases into landscapes of viral fitness. These inferred models furnish a quantitative description of viral replicative capacity as a function of amino acid sequence. We illustrate this approach in the development of landscapes for the proteins of HIV-1 clade B Gag. Results In comparisons to experimental and clinical data, our inferred landscapes demonstrate excellent agreement with: 1) in vitro replicative fitness measurements, 2) clinically observed high-fitness circulating viral strains, 3) documented HLA associated CTL escape mutations, and 4) intrahost temporal adaptation pathways revealed by deep sequencing. These favorable comparisons support our landscapes as reflections of intrinsic viral fitness. We illustrate the value of such descriptions in the computational design of a CTL Gag immunogen. Conclusion We present a novel methodology to translate viral sequence data into quantitative landscapes of viral fitness. In an application to HIV-1 Gag, we illustrate excellent agreement of our model predictions with experimental and clinical data, and demonstrate a powerful new approach for HIV immunogen design. We anticipate that this approach may represent a heretofore unprecedented means to synthesize fitness landscapes for diverse pathogens, and may provide the basis for the design of improved prophylactic and therapeutic strategies.

Effects of point substitutions on the structure of toxic Alzheimer's β-amyloid channels: atomic force microscopy and molecular dynamics simulations.

Alzheimer's disease (AD) is a misfolded protein disease characterized by the accumulation of β-amyloid (Aβ) peptide as senile plaques, progressive neurodegeneration, and memory loss. Recent evidence suggests that AD pathology is linked to the destabilization of cellular ionic homeostasis mediated by toxic pores made of Aβ peptides. Understanding the exact nature by which these pores conduct electrical and molecular signals could aid in identifying potential therapeutic targets for the prevention and treatment of AD. Here using atomic force microscopy (AFM) and molecular dynamics (MD) simulations, we compared the imaged pore structures with models to predict channel conformations as a function of amino acid sequence. Site-specific amino acid (AA) substitutions in the wild-type Aβ(1-42) peptide yield information regarding the location and significance of individual AA residues to its characteristic structure-activity relationship. We selected two AAs that our MD simulation predicted to inhibit or permit pore conductance. The substitution of Phe19 with Pro has previously been shown to eliminate conductance in the planar lipid bilayer system. Our MD simulations predict a channel-like shape with a collapsed pore, which is supported by the AFM channel images. We suggest that proline, a known β-sheet breaker, creates a kink in the center of the pore and prevents conductance via blockage. This residue may be a viable target for drug development studies aiming to inhibit Aβ from inducing ionic destabilization toxicity. The substitution of Phe20 with Cys exhibits pore structures indistinguishable from the wild type in AFM images. MD simulations predict site 20 to face the solvated pore. Overall, the mutations support the previously predicted β-sheet-based channel structure.

Solution Structure of the Guanine Nucleotide-binding STAS Domain of SLC26-related SulP Protein Rv1739c from Mycobacterium tuberculosis

The structure and intrinsic activities of conserved STAS domains of the ubiquitous SulP/SLC26 anion transporter superfamily have until recently remained unknown. Here we report the heteronuclear, multidimensional NMR spectroscopy solution structure of the STAS domain from the SulP/SLC26 putative anion transporter Rv1739c of Mycobacterium tuberculosis. The 0.87-Å root mean square deviation structure revealed a four-stranded β-sheet with five interspersed α-helices, resembling the anti-σ factor antagonist fold. Rv1739c STAS was shown to be a guanine nucleotide-binding protein, as revealed by nucleotide-dependent quench of intrinsic STAS fluorescence and photoaffinity labeling. NMR chemical shift perturbation analysis partnered with in silico docking calculations identified solvent-exposed STAS residues involved in nucleotide binding. Rv1739c STAS was not an in vitro substrate of mycobacterial kinases or anti-σ factors. These results demonstrate that Rv1739c STAS binds guanine nucleotides at physiological concentrations and undergoes a ligand-induced conformational change but, unlike anti-σ factor antagonists, may not mediate signals via phosphorylation.

Computational Design of a Self-Assembling β-Peptide Oligomer

The first computationally designed self-assembling oligomer consisting of exclusively β-amino acids (βAAs) is presented. The packing of a β-3(14) helix into coiled-coils of varying stoichiometries as a function of amino acid sequence is examined. β-Peptides with hVal repeating every third residue in the sequence appeared to have a strong propensity to pack into hexameric bundles. The designed sequence was synthesized and characterized with CD spectroscopy, NMR, and analytical ultracentrifugation, suggesting that the peptide adopts a well-folded hexameric structure.

Novel Index of Polar Amino Acids Characterizing End Region of Transmembrane Helices

Index of amino acids is useful for extracting amino acid preference, which have large statistical noise [1]. Peaks of local average of appropriate index indicate clusters of preferable amino acids, because the statistical noise may be reduced by the averaging procedure. For example, hydrophobicity (hydropathy) index is very useful for visualizing hydrophobic segments in transmembrane helices [2]. However, characteristic preference of polar amino acids at the end regions of transmembrane helices has not been indexed for far. The propensity study of amino acids membrane proteins indicated that the polar residues, lysine, arginine and tryptophan, are preferable to the end region of transmembrane helices. Therefore, this preference of amino acids may become useful parameter for the prediction of transmembrane helical segments [3, 4]. In this work, a novel index of polar amino acids, which we call amphiphilicity index. The amphiphilicity index has finite values for larger polar residues (K, R, H, E, Q, W and Y), whereas the index was zero for small polar residues and hydrophobic residues. The value of amphiphilicity index showed good correlation with the propensity of amino acids at the end regions. When the local average of seven-residue window was plotted as a function of amino acid sequences, most peaks of the amphiphilicity index contained the end points of transmembrane helices, suggesting the possibility to improve the accuracy of transmembrane prediction.

Peptide Folding: When Simulation Meets Experiment

Accurate reproduction of the mechanism of peptide folding in solution and conformational preferences as a function of amino acid sequence is possible with atomic level dynamics simulations. For example, the simulations correctly predict a left-handed 31-helical fold for the β-heptapeptide 1 (the molecular model is shown in the picture) and a right-handed helical fold for the β-hexapeptide 2, as was confirmed by NMR spectroscopy.

Anti-inflammatory activity of superoxide dismutases: inhibition of carrageenan induced edema in rats.

Eighteen different superoxide dismutases from procaryote, plant, fish, bird and mammalian species have been tested for anti-inflammatory activity in the rat paw pad carrageenan-induced inflammation model. Very large differences in activity are observed. Homologous rat Cu-SOD is not active and indeed shows slight pro-inflammatory activity. The different SODs have different iso-electric values, different metals (Cu, Mn or Fe) at the active centre, different molecular weights and different circulation lifetimes. Biological activity is a function of amino acid sequence rather than of such secondary parameters.