The design and characterization of two proteins with 88% sequence identity but different structure and function

Amino-Acid Characteristics in Protein Native State Structures

The molecular machines of life, proteins, are made up of twenty kinds of amino acids, each with distinctive side chains. We present a geometrical analysis of the protrusion statistics of side chains in more than 4000 high-resolution protein structures. We employ a coarse-grained representation of the protein backbone viewed as a linear chain of Cα atoms and consider just the heavy atoms of the side chains. We study the large variety of behaviors of the amino acids based on both rudimentary structural chemistry as well as geometry. Our geometrical analysis uses a backbone Frenet coordinate system for the common study of all amino acids. Our analysis underscores the richness of the repertoire of amino acids that is available to nature to design protein sequences that fit within the putative native state folds.

Predicting horizontal gene transfers with perfect transfer networks

BACKGROUND

Horizontal gene transfer inference approaches are usually based on gene sequences: parametric methods search for patterns that deviate from a particular genomic signature, while phylogenetic methods use sequences to reconstruct the gene and species trees. However, it is well-known that sequences have difficulty identifying ancient transfers since mutations have enough time to erase all evidence of such events. In this work, we ask whether character-based methods can predict gene transfers. Their advantage over sequences is that homologous genes can have low DNA similarity, but still have retained enough important common motifs that allow them to have common character traits, for instance the same functional or expression profile. A phylogeny that has two separate clades that acquired the same character independently might indicate the presence of a transfer even in the absence of sequence similarity.

OUR CONTRIBUTIONS

We introduce perfect transfer networks, which are phylogenetic networks that can explain the character diversity of a set of taxa under the assumption that characters have unique births, and that once a character is gained it is rarely lost. Examples of such traits include transposable elements, biochemical markers and emergence of organelles, just to name a few. We study the differences between our model and two similar models: perfect phylogenetic networks and ancestral recombination networks. Our goals are to initiate a study on the structural and algorithmic properties of perfect transfer networks. We then show that in polynomial time, one can decide whether a given network is a valid explanation for a set of taxa, and show how, for a given tree, one can add transfer edges to it so that it explains a set of taxa. We finally provide lower and upper bounds on the number of transfers required to explain a set of taxa, in the worst case.

Predicting multiple conformations via sequence clustering and AlphaFold2

AlphaFold2 (ref. 1) has revolutionized structural biology by accurately predicting single structures of proteins. However, a protein's biological function often depends on multiple conformational substates2, and disease-causing point mutations often cause population changes within these substates3,4. We demonstrate that clustering a multiple-sequence alignment by sequence similarity enables AlphaFold2 to sample alternative states of known metamorphic proteins with high confidence. Using this method, named AF-Cluster, we investigated the evolutionary distribution of predicted structures for the metamorphic protein KaiB5 and found that predictions of both conformations were distributed in clusters across the KaiB family. We used nuclear magnetic resonance spectroscopy to confirm an AF-Cluster prediction: a cyanobacteria KaiB variant is stabilized in the opposite state compared with the more widely studied variant. To test AF-Cluster's sensitivity to point mutations, we designed and experimentally verified a set of three mutations predicted to flip KaiB from Rhodobacter sphaeroides from the ground to the fold-switched state. Finally, screening for alternative states in protein families without known fold switching identified a putative alternative state for the oxidoreductase Mpt53 in Mycobacterium tuberculosis. Further development of such bioinformatic methods in tandem with experiments will probably have a considerable impact on predicting protein energy landscapes, essential for illuminating biological function.

SSDraw: Software for generating comparative protein secondary structure diagrams

The program SSDraw generates publication-quality protein secondary structure diagrams from three-dimensional protein structures. To depict relationships between secondary structure and other protein features, diagrams can be colored by conservation score, B-factor, or custom scoring. Diagrams of homologous proteins can be registered according to an input multiple sequence alignment. Linear visualization allows the user to stack registered diagrams, facilitating comparison of secondary structure and other properties among homologous proteins. SSDraw can be used to compare secondary structures of homologous proteins with both conserved and divergent folds. It can also generate one secondary structure diagram from an input protein structure of interest. The source code can be downloaded (https://github.com/ncbi/SSDraw) and run locally for rapid structure generation, while a Google Colab notebook allows easy use.

SSDraw: software for generating comparative protein secondary structure diagrams

The program SSDraw generates publication-quality protein secondary structure diagrams from three-dimensional protein structures. To depict relationships between secondary structure and other protein features, diagrams can be colored by conservation score, B-factor, or custom scoring. Diagrams of homologous proteins can be registered according to an input multiple sequence alignment. Linear visualization allows the user to stack registered diagrams, facilitating comparison of secondary structure and other properties among homologous proteins. SSDraw can be used to compare secondary structures of homologous proteins with both conserved and divergent folds. It can also generate one secondary structure diagram from an input protein structure of interest. The source code can be downloaded (https://github.com/ethanchen1301/SSDraw) and run locally for rapid structure generation, while a Google Colab notebook allows easy use.

HNSPPI: a hybrid computational model combing network and sequence information for predicting protein-protein interaction

Most life activities in organisms are regulated through protein complexes, which are mainly controlled via Protein-Protein Interactions (PPIs). Discovering new interactions between proteins and revealing their biological functions are of great significance for understanding the molecular mechanisms of biological processes and identifying the potential targets in drug discovery. Current experimental methods only capture stable protein interactions, which lead to limited coverage. In addition, expensive cost and time consuming are also the obvious shortcomings. In recent years, various computational methods have been successfully developed for predicting PPIs based only on protein homology, primary sequences of protein or gene ontology information. Computational efficiency and data complexity are still the main bottlenecks for the algorithm generalization. In this study, we proposed a novel computational framework, HNSPPI, to predict PPIs. As a hybrid supervised learning model, HNSPPI comprehensively characterizes the intrinsic relationship between two proteins by integrating amino acid sequence information and connection properties of PPI network. The experimental results show that HNSPPI works very well on six benchmark datasets. Moreover, the comparison analysis proved that our model significantly outperforms other five existing algorithms. Finally, we used the HNSPPI model to explore the SARS-CoV-2-Human interaction system and found several potential regulations. In summary, HNSPPI is a promising model for predicting new protein interactions from known PPI data.

A geometrical framework for thinking about proteins

We present a model, based on symmetry and geometry, for proteins. Using elementary ideas from mathematics and physics, we derive the geometries of discrete helices and sheets. We postulate a compatible solvent-mediated emergent pairwise attraction that assembles these building blocks, while respecting their individual symmetries. Instead of seeking to mimic the complexity of proteins, we look for a simple abstraction of reality that yet captures the essence of proteins. We employ analytic calculations and detailed Monte Carlo simulations to explore some consequences of our theory. The predictions of our approach are in accord with experimental data. Our framework provides a rationalization for understanding the common characteristics of proteins. Our results show that the free energy landscape of a globular protein is pre-sculpted at the backbone level, sequences and functionalities evolve in the fixed backdrop of the folds determined by geometry and symmetry, and that protein structures are unique in being simultaneously characterized by stability, diversity, and sensitivity.

Identification of a covert evolutionary pathway between two protein folds

Although homologous protein sequences are expected to adopt similar structures, some amino acid substitutions can interconvert α-helices and β-sheets. Such fold switching may have occurred over evolutionary history, but supporting evidence has been limited by the: (1) abundance and diversity of sequenced genes, (2) quantity of experimentally determined protein structures, and (3) assumptions underlying the statistical methods used to infer homology. Here, we overcome these barriers by applying multiple statistical methods to a family of ~600,000 bacterial response regulator proteins. We find that their homologous DNA-binding subunits assume divergent structures: helix-turn-helix versus α-helix + β-sheet (winged helix). Phylogenetic analyses, ancestral sequence reconstruction, and AlphaFold2 models indicate that amino acid substitutions facilitated a switch from helix-turn-helix into winged helix. This structural transformation likely expanded DNA-binding specificity. Our approach uncovers an evolutionary pathway between two protein folds and provides a methodology to identify secondary structure switching in other protein families.

New insights on the catalytic center of proteins from peptidylprolyl isomerase group based on the FOD-M model

Generating the structure of the hydrophobic core is based on the orientation of hydrophobic residues towards the central part of the protein molecule with the simultaneous exposure of polar residues. Such a course of the protein folding process takes place with the active participation of the polar water environment. While the self-assembly process leading to the formation of micelles concerns freely moving bi-polar molecules, bipolar amino acids in polypeptide chain have limited mobility due to the covalent bonds. Therefore, proteins form a more or less perfect micelle-like structure. The criterion is the hydrophobicity distribution, which to a greater or lesser extent reproduces the distribution expressed by the 3D Gaussian function on the protein body. The vast majority of proteins must ensure solubility, so a certain part of it-as it is expected-should reproduce the structuring of micelles. The biological activity of proteins is encoded in the part that does not reproduce the micelle-like system. The location and quantitative assessment of the contribution of orderliness to disorder is of critical importance for the determination of biological activity. The form of maladjustment to the 3D Gauss function may be varied-hence the obtained high diversity of specific interactions with strictly defined molecules: ligands or substrates. The correctness of this interpretation was verified on the basis of the group of enzymes Peptidylprolyl isomerase-E.C.5.2.1.8. In proteins representing this class of enzymes, zones responsible for solubility-micelle-like hydrophobicity system-the location and specificity of the incompatible part in which the specific activity of the enzyme is located and coded were identified. The present study showed that the enzymes of the discussed group show two different schemes of the structure of catalytic center (taking into account the status as defined by the fuzzy oil drop model).

A review of visualisations of protein fold networks and their relationship with sequence and function

Proteins form arguably the most significant link between genotype and phenotype. Understanding the relationship between protein sequence and structure, and applying this knowledge to predict function, is difficult. One way to investigate these relationships is by considering the space of protein folds and how one might move from fold to fold through similarity, or potential evolutionary relationships. The many individual characterisations of fold space presented in the literature can tell us a lot about how well the current Protein Data Bank represents protein fold space, how convergence and divergence may affect protein evolution, how proteins affect the whole of which they are part, and how proteins themselves function. A synthesis of these different approaches and viewpoints seems the most likely way to further our knowledge of protein structure evolution and thus, facilitate improved protein structure design and prediction.

Design and characterization of a protein fold switching network

To better understand how amino acid sequence encodes protein structure, we engineered mutational pathways that connect three common folds (3α, β-grasp, and α/β-plait). The structures of proteins at high sequence-identity intersections in the pathways (nodes) were determined using NMR spectroscopy and analyzed for stability and function. To generate nodes, the amino acid sequence encoding a smaller fold is embedded in the structure of an ~50% larger fold and a new sequence compatible with two sets of native interactions is designed. This generates protein pairs with a 3α or β-grasp fold in the smaller form but an α/β-plait fold in the larger form. Further, embedding smaller antagonistic folds creates critical states in the larger folds such that single amino acid substitutions can switch both their fold and function. The results help explain the underlying ambiguity in the protein folding code and show that new protein structures can evolve via abrupt fold switching.

Reversible switching between two common protein folds in a designed system using only temperature

Naturally occurring metamorphic proteins have the ability to interconvert from one folded state to another through either a limited set of mutations or by way of a change in the local environment. Here, we show in a designed system that it is possible to switch reversibly between two of the most common monomeric folds employing only temperature changes. We demonstrate that a latent 3α state can be unmasked from an α/β-plait topology with a single V90T amino acid substitution, populating both forms simultaneously. The equilibrium between these two states exhibits temperature dependence, such that the 3α state is predominant (>90%) at 5 °C, while the α/β-plait fold is the major species (>90%) at 30 °C. We describe the structure and dynamics of these topologies, how mutational changes affect the temperature dependence, and the energetics and kinetics of interconversion. Additionally, we demonstrate how ligand-binding function can be tightly regulated by large amplitude changes in protein structure over a relatively narrow temperature range that is relevant to biology. The 3α/αβ switch thus represents a potentially useful approach for designing proteins that alter their fold topologies in response to environmental triggers. It may also serve as a model for computational studies of temperature-dependent protein stability and fold switching.

An Outlook on the Complexity of Protein Morphogenesis in Health and Disease

The study of the mechanisms whereby proteins achieve their native functionally competent conformation has been a key issue in molecular biosciences over the last 6 decades. Nevertheless, there are several debated issues and open problems concerning some aspects of this fundamental problem. By considering the emerging complexity of the so-called “native state,” we attempt hereby to propose a personal account on some of the key topics in the field, ranging from the relationships between misfolding and diseases to the significance of protein disorder. Finally, we briefly describe the recent and exciting advances in predicting protein structures from their amino acid sequence.

Exploring the Effect of Enhanced Sampling on Protein Stability Prediction

Changes in protein stability due to side-chain mutations are evaluated by alchemical free-energy calculations based on classical molecular dynamics (MD) simulations in explicit solvent using the GROMOS force field. Three proteins of different complexity with a total number of 93 single-point mutations are analyzed, and the relative free-energy differences are discussed with respect to configurational sampling and (dis)agreement with experimental data. For the smallest protein studied, a 34-residue WW domain, the starting structure dependence of the alchemical free-energy changes, is discussed in detail. Deviations from previous simulations for the two other proteins are shown to result from insufficient sampling in the earlier studies. Hamiltonian replica exchange in combination with multiple starting structures and sufficient sampling time of more than 100 ns per intermediate alchemical state is required in some cases to reach convergence.

The stability and dynamics of computationally designed proteins

Protein stability, dynamics and function are intricately linked. Accordingly, protein designers leverage dynamics in their designs and gain insight to their successes and failures by analyzing their proteins' dynamics. Molecular dynamics (MD) simulations are a powerful computational tool for quantifying both local and global protein dynamics. This review highlights studies where MD simulations were applied to characterize the stability and dynamics of designed proteins and where dynamics were incorporated into computational protein design. First, we discuss the structural basis underlying the extreme stability and thermostability frequently observed in computationally designed proteins. Next, we discuss examples of designed proteins, where dynamics were not explicitly accounted for in the design process, whose coordinated motions or active site dynamics, as observed by MD simulation, enhanced or detracted from their function. Many protein functions depend on sizeable or subtle conformational changes, so we finally discuss the computational design of proteins to perform a specific function that requires consideration of motion by multi-state design.

The nature of persistent interactions in two model β-grasp proteins reveals the advantage of symmetry in stability

Two proteins within the β-grasp superfamily, the B1-domain of protein G and the small archaeal modifier protein 1, were investigated to elucidate the key determinants of structural stability at the level of individual interactions. These symmetrical proteins both contain two β-hairpins which form a sheet flanked by a central α-helix. They were subjected to high temperature molecular dynamics simulations and the detailed behavior of each long-range interaction was characterized. The results revealed that in GB1 the most stable region was the C-terminal hairpin and in SAMP1 it was the opposite, the N-terminal hairpin. Experimental results for GB1 support this finding. In conclusion, it appears that the difference in the location and number of hydrophobic interactions dictate the differential stability which is accommodated due to structural symmetry of the β-grasp fold. Thus, the hairpins are interchangeable and in nature this lends itself to adaptability and flexibility.

Local sequence-structure relationships in proteins

We seek to understand the interplay between amino acid sequence and local structure in proteins. Are some amino acids unique in their ability to fit harmoniously into certain local structures? What is the role of sequence in sculpting the putative native state folds from myriad possible conformations? In order to address these questions, we represent the local structure of each Cα atom of a protein by just two angles, θ and μ, and we analyze a set of more than 4,000 protein structures from the PDB. We use a hierarchical clustering scheme to divide the 20 amino acids into six distinct groups based on their similarity to each other in fitting local structural space. We present the results of a detailed analysis of patterns of amino acid specificity in adopting local structural conformations and show that the sequence-structure correlation is not very strong compared with a random assignment of sequence to structure. Yet, our analysis may be useful to determine an effective scoring rubric for quantifying the match of an amino acid to its putative local structure.

Engineering subtilisin proteases that specifically degrade active RAS

We describe the design, kinetic properties, and structures of engineered subtilisin proteases that degrade the active form of RAS by cleaving a conserved sequence in switch 2. RAS is a signaling protein that, when mutated, drives a third of human cancers. To generate high specificity for the RAS target sequence, the active site was modified to be dependent on a cofactor (imidazole or nitrite) and protease sub-sites were engineered to create a linkage between substrate and cofactor binding. Selective proteolysis of active RAS arises from a 2-step process wherein sub-site interactions promote productive binding of the cofactor, enabling cleavage. Proteases engineered in this way specifically cleave active RAS in vitro, deplete the level of RAS in a bacterial reporter system, and also degrade RAS in human cell culture. Although these proteases target active RAS, the underlying design principles are fundamental and will be adaptable to many target proteins.

Structure and function of naturally evolved de novo proteins

Comparative evolutionary genomics has revealed that novel protein coding genes can emerge randomly from non-coding DNA. While most of the myriad of transcripts which continuously emerge vanish rapidly, some attain regulatory regions, become translated and survive. More surprisingly, sequence properties of de novo proteins are almost indistinguishable from randomly obtained sequences, yet de novo proteins may gain functions and integrate into eukaryotic cellular networks quite easily. We here discuss current knowledge on de novo proteins, their structures, functions and evolution. Since the existence of de novo proteins seems at odds with decade-long attempts to construct proteins with novel structures and functions from scratch, we suggest that a better understanding of de novo protein evolution may fuel new strategies for protein design.

Process, Outcomes and Possible Elimination of Aggregation with Special Reference to Heme Proteins; Likely Remediations of Proteinopathies

Protein folding is a natural phenomenon through which a linear polypeptide possessing necessary information attains three-dimension functionally active conformation. This is a complex and multistep process and therefore, the presence of several intermediary structures could be speculated as a result of protein folding. In in vivo, this folding process is governed by the assistance of other proteins called molecular chaperones and heat shock proteins. Due to the mechanism of protein folding, these intermediary structures remain major challenge for modern biology. Mutation in gene encoding amino acid can cause adverse environmental conditions which may result in misfolding of the linear polypeptide followed by the formation of aggregates and amyloidosis. Aggregation contributes to the pathophysiology of several maladies including diabetes mellitus, Huntington's and Alzheimer's disease. The propensity of native structure to form aggregated and fibrillar assemblies is a hallmark of amyloidosis. During aggregation of a protein, transition from α helix to β sheet is observed, and mainly β sheeted structure is visualised in a mature fibril. Heme proteins are very crucial for major life activities like transport of oxygen and carbon dioxide, synthesis of ATP, role in electron transport chain, and detoxification of free radicals formed during biochemical reactions. Any structural variation in the heme proteins may lead to a fatal response. Hence characterization of the folding intermediates becomes crucial. The characterization has been deciphered with the help of strong denaturants like acetonitrile and TFE. Moreover, possible role of elimination of these aggregates and prevention of protein denaturation is also discussed. Current review deals with the basic process and mechanism of the protein folding in general and the ultimate outcomes of the protein misfolding. Since Native conformation of heme proteins is essential for some vital activities as listed above, we have discussed possible prevention of denaturation and aggregation of heme proteins such as Hb, cyt c, catalase & peroxidase.

Single molecule force spectroscopy reveals the context dependent folding pathway of the C-terminal fragment of Top7

Top7 is a de novo designed protein with atomic level accuracy and shows a folded structure not found in nature. Previous studies showed that the folding of Top7 is not cooperative and involves various folding intermediate states. In addition, various fragments of Top7 were found to fold on their own in isolation. These features displayed by Top7 are distinct from those of naturally occurring proteins of a similar size and suggest a rough folding energy landscape. However, it remains unknown if and how the intra-polypeptide chain interactions among the neighboring sequences of Top7 affect the folding of these Top7 fragments. Here we used single-molecule optical tweezers to investigate the folding–unfolding pathways of full length Top7 as well as its C-terminal fragment (CFr) in different sequence environments. Our results showed that the mechanical folding of Top7 involves an intermediate state that likely involves non-native interactions/structure. More importantly, we found that the folding of CFr is entirely dependent upon its sequence context in which it is located. When in isolation, CFr indeed folds into a cooperative structure showing near-equilibrium unfolding–folding transitions at ∼6.5 pN in OT experiments. However, CFr loses its autonomous cooperative folding ability and displays a folding pathway that is dependent on its interactions with its neighboring sequence/structure. This context-dependent folding dynamics and pathway of CFr are distinct from those of naturally occurring proteins and highlight the critical importance of intra-chain interactions in shaping the overall energy landscape and the folding pathway of Top7. These new insights may have important implications on the de novo design of proteins.

Optical tweezers experiments reveal that the folding of the C-terminal fragment of Top7 (cFr) is context-dependent. Depending on its neighboring sequence, cFr shows very different folding pathways and folding kinetics.

Characterization of binding between model protein GA-Z and human serum albumin using asymmetrical flow field-flow fractionation and small angle X-ray scattering

Protein-based drugs often require targeted drug delivery for optimal therapy. A successful strategy to increase the circulation time of the protein in the blood is to link the therapeutic protein with an albumin-binding domain. In this work, we characterized such a protein-based drug, GA-Z. Using asymmetrical flow field-flow fractionation coupled with multi-angle light scattering (AF4-MALS) we investigated the GA-Z monomer-dimer equilibrium as well as the molar binding ratio of GA-Z to HSA. Using small angle X-ray scattering, we studied the structure of GA-Z as well as the complex between GA-Z and HSA. The results show that GA-Z is predominantly dimeric in solution at pH 7 and that it binds to monomeric as well as dimeric HSA. Furthermore, GA-Z binds to HSA both as a monomer and a dimer, and thus, it can be expected to stay bound also upon dilution following injection in the blood stream. The results from SAXS and binding studies indicate that the GA-Z dimer is formed between two target domains (Z-domains). The results also indicate that the binding of GA-Z to HSA does not affect the ratio between HSA dimers and monomers, and that no higher order oligomers of the complex are seen other than those containing dimers of GA-Z and dimers of HSA.

Computational pipeline to probe NaV1.7 gain-of-function variants in neuropathic painful syndromes

Applications of machine learning and graph theory techniques to neuroscience have witnessed an increased interest in the last decade due to the large data availability and unprecedented technology developments. Their employment to investigate the effect of mutational changes in genes encoding for proteins modulating the membrane of excitable cells, whose biological correlates are assessed at electrophysiological level, could provide useful predictive clues. We apply this concept to the analysis of variants in sodium channel NaV1.7 subunit found in patients with chronic painful syndromes, by the implementation of a dedicated computational pipeline empowering different and complementary techniques including homology modeling, network theory, and machine learning. By testing three templates of different origin and sequence identities, we provide an optimal condition for its use. Our findings reveal the usefulness of our computational pipeline in supporting the selection of candidates for cell electrophysiology assay and with potential clinical applications.

Exploring the sequence fitness landscape of a bridge between protein folds

Most foldable protein sequences adopt only a single native fold. Recent protein design studies have, however, created protein sequences which fold into different structures apon changes of environment, or single point mutation, the best characterized example being the switch between the folds of the GA and GB binding domains of streptococcal protein G. To obtain further insight into the design of sequences which can switch folds, we have used a computational model for the fitness landscape of a single fold, built from the observed sequence variation of protein homologues. We have recently shown that such coevolutionary models can be used to design novel foldable sequences. By appropriately combining two of these models to describe the joint fitness landscape of GA and GB, we are able to describe the propensity of a given sequence for each of the two folds. We have successfully tested the combined model against the known series of designed GA/GB hybrids. Using Monte Carlo simulations on this landscape, we are able to identify pathways of mutations connecting the two folds. In the absence of a requirement for domain stability, the most frequent paths go via sequences in which neither domain is stably folded, reminiscent of the propensity for certain intrinsically disordered proteins to fold into different structures according to context. Even if the folded state is required to be stable, we find that there is nonetheless still a wide range of sequences which are close to the transition region and therefore likely fold switches, consistent with recent estimates that fold switching may be more widespread than had been thought.

Structure of the Hydrophobic Core Determines the 3D Protein Structure-Verification by Single Mutation Proteins

Four de novo proteins differing in single mutation positions, with a chain length of 56 amino acids, represent diverse 3D structures: monomeric 3α and 4β + α folds. The reason for this diversity is seen in the different structure of the hydrophobic core as a result of synergy leading to the generation of a system in which the polypeptide chain as a whole participates. On the basis of the fuzzy oil drop model, where the structure of the hydrophobic core is expressed by means of the hydrophobic distribution function in the form of a 3D Gaussian distribution, it has been shown that the composition of the hydrophobic core in these two structural forms is different. In addition, the use of a model to determine the structure of the early intermediate in the folding process allows to indicate differences in the polypeptide chain geometry, which, combined with the construction of a common hydrophobic nucleus as an effect of specific synergy, may indicate the reason for the diversity of the folding process of the polypeptide chain. The results indicate the need to take into account the presence of an external force field originating from the water environment and that its active impact on the formation of a hydrophobic core whose participation in the stabilization of the tertiary structure is fundamental.

Alternative Hydrophobic Core in Proteins—The Effect of Specific Synergy

Proteins with a high degree of sequence similarity representing different structures provide a key to understand how protein sequence codes for 3D structure. An analysis using the fuzzy oil drop model was carried out on two pairs of proteins with different secondary structures and with high sequence identities. It has been shown that distributions of hydrophobicity for these proteins are approximated well using single 3D Gaussian function. In other words, the similar sequences fold into different 3D structures, however, alternative structures also have symmetric and monocentric hydrophobic cores. It should be noted that a significant change in the helical to beta-structured form in the N-terminal section takes places in the fragment much preceding the location of the mutated regions. It can be concluded that the final structure is the result of a complicated synergy effect in which the whole chain participates simultaneously.

Evolutionary Switches Structural Transitions via Coarse-Grained Models

Transitions between different conformational states are ubiquitous in proteins. A vast class of conformation-changing proteins includes evolutionary switches, which vary their conformation as an effect of few mutations or weak environmental variations. However, modeling those processes is extremely difficult due to the need of efficiently exploring a vast conformational space to look for the actual transition path. In this study, we report a strategy that simplifies this task attacking the complexity on several sides. We first apply a minimalist coarse-grained model to the protein, based on an empirical force field with a partial structural bias toward one or both the reference structures. We then explore the transition paths by means of stochastic molecular dynamics and select representative structures by means of a principal path-based clustering algorithm. We finally compare this trajectory with that produced by independent methods adopting a morphing-oriented approach. Our analysis indicates that the minimalist model returns trajectories capable of exploring intermediate states with physical meaning, retaining a very low computational cost, which can allow systematic and extensive exploration of the multistable proteins transition pathways.

A multifunnel energy landscape encodes the competing α-helix and β-hairpin conformations for a designed peptide

Depending on the amino acid sequence, as well as the local environment, some peptides have the capability to fold into multiple secondary structures. Conformational switching between such structures is a key element of protein folding and aggregation. Specifically, understanding the molecular mechanism underlying the transition from an α-helix to a β-hairpin is critical because it is thought to be a harbinger of amyloid assembly. In this study, we explore the energy landscape for an 18-residue peptide (DP5), designed by Araki and Tamura to exhibit equal propensities for the α-helical and β-hairpin forms. We find that the degeneracy is encoded in the multifunnel nature of the underlying free energy landscape. In agreement with experiment, we also observe that mutation of tyrosine at position 12 to serine shifts the equilibrium in favor of the α-helix conformation, by altering the landscape topography. The transition from the α-helix to the β-hairpin is a complex stepwise process, and occurs via collapsed coil-like intermediates. Our findings suggest that even a single mutation can tune the emergent features of the landscape, providing an efficient route to protein design. Interestingly, the transition pathways for the conformational switch seem to be minimally perturbed upon mutation, suggesting that there could be universal microscopic features that are conserved among different switch-competent protein sequences.

Latest trends in structure based drug design with protein targets

Structure based drug designing is an important endeavor in the field of structural bioinformatics. Previously the entire process was dependent on the wet-lab experiments to build libraries of ligand molecules. And the molecules used to be tested to determine their binding efficacies with protein target. However, the entire process is very lengthy and above all highly expensive. With the advent of supercomputers and increasing computational powers, the search process for finding suitable ligand molecules against target proteins have become more streamlined and cost-effective. Now the entire ligand search process is performed in-silico with the help of the techniques of virtual screening, molecular docking simulations and molecular dynamics studies. In the present chapter, a brief overview of the computational techniques involved in structure based drug designing is presented with a special emphasis on the thermodynamic principles behind the molecular interactions.

Design of catalytic polypeptides and proteins

The first part of this review article lists examples of complete, empirical de novo design that made important contributions to the development of the field and initiated challenging projects. The second part of this article deals with computational design of novel enzymes in native protein scaffolds; active designs were refined through random and site-directed mutagenesis producing artificial enzymes with nearly native enzyme- like activities against a number of non-natural substrates. Combining aspects of de novo design and biological evolution of nature's enzymes has started and will accelerate the development of novel enzyme activities.

Multistep mutational transformation of a protein fold through structural intermediates

New protein folds may evolve from existing folds through metamorphic evolution involving a dramatic switch in structure. To mimic pathways by which amino acid sequence changes could induce a change in fold, we designed two folded hybrids of Xfaso 1 and Pfl 6, a pair of homologous Cro protein sequences with ~40% identity but different folds (all-α vs. α + β, respectively). Each hybrid, XPH1 or XPH2, is 85% identical in sequence to its parent, Xfaso 1 or Pfl 6, respectively; 55% identical to its noncognate parent; and ~70% identical to the other hybrid. XPH1 and XPH2 also feature a designed hybrid chameleon sequence corresponding to the C-terminal region, which switched from α-helical to β-sheet structure during Cro evolution. We report solution nuclear magnetic resonance (NMR) structures of XPH1 and XPH2 at 0.3 Å and 0.5 Å backbone root mean square deviation (RMSD), respectively. XPH1 retains a global fold generally similar to Xfaso 1, and XPH2 retains a fold similar to Pfl 6, as measured by TM-align scores (~0.7), DALI Z-scores (7-9), and backbone RMSD (2-3 Å RMSD for the most ordered regions). However, these scores also indicate significant deviations in structure. Most notably, XPH1 and XPH2 have different, and intermediate, secondary structure content relative to Xfaso 1 and Pfl 6. The multistep progression in sequence, from Xfaso 1 to XPH1 to XPH2 to Pfl 6, thus involves both abrupt and gradual changes in folding pattern. The plasticity of some protein folds may allow for "polymetamorphic" evolution through intermediate structures.

Structural metamorphism and polymorphism in proteins on the brink of thermodynamic stability

The classical view of the structure-function paradigm advanced by Anfinsen in the 1960s is that a protein's function is inextricably linked to its three-dimensional structure and is encrypted in its amino acid sequence. However, it is now known that a significant fraction of the proteome consists of intrinsically disordered proteins (IDPs). These proteins populate a polymorphic ensemble of conformations rather than a unique structure but are still capable of performing biological functions. At the boundary, between well-ordered and inherently disordered states are proteins that are on the brink of stability, either weakly stable ordered systems or disordered but on the verge of being stable. In such marginal states, even relatively minor changes can significantly alter the energy landscape, leading to large-scale conformational remodeling. Some proteins on the edge of stability are metamorphic, with the capacity to switch from one fold topology to another in response to an environmental trigger (e.g., pH, temperature/salt, redox). Many IDPs, on the other hand, are marginally unstable such that small perturbations (e.g., phosphorylation, ligands) tip the balance over to a range of ordered, partially ordered, or even more disordered states. In general, the structural transitions described by metamorphic fold switches and polymorphic IDPs possess a number of common features including low or diminished stability, large-scale conformational changes, critical disordered regions, latent or attenuated binding sites, and expansion of function. We suggest that these transitions are, therefore, conceptually and mechanistically analogous, representing adjacent regions in the continuum of order/disorder transitions.

On the efficiency of the genetic code after frameshift mutations

Statistical and biochemical studies of the standard genetic code (SGC) have found evidence that the impact of mistranslations is minimized in a way that erroneous codes are either synonymous or code for an amino acid with similar polarity as the originally coded amino acid. It could be quantified that the SGC is optimized to protect this specific chemical property as good as possible. In recent work, it has been speculated that the multilevel optimization of the genetic code stands in the wider context of overlapping codes. This work tries to follow the systematic approach on mistranslations and to extend those analyses to the general effect of frameshift mutations on the polarity conservation of amino acids. We generated one million random codes and compared their average polarity change over all triplets and the whole set of possible frameshift mutations. While the natural code—just as for the point mutations—appears to be competitively robust against frameshift mutations as well, we found that both optimizations appear to be independent of each other. For both, better codes can be found, but it becomes significantly more difficult to find candidates that optimize all of these features—just like the SGC does. We conclude that the SGC is not only very efficient in minimizing the consequences of mistranslations, but rather optimized in amino acid polarity conservation for all three effects of code alteration, namely translational errors, point and frameshift mutations. In other words, our result demonstrates that the SGC appears to be much more than just “one in a million”.

Multifunnel Landscape of the Fold-Switching Protein RfaH-CTD

Proteins such as the transcription factor RfaH can change biological function by switching between distinct three-dimensional folds. RfaH regulates transcription if the C-terminal domain folds into a double helix bundle and promotes translation when this domain assumes a β-barrel form. This fold-switch has been also observed for the isolated C-terminal domain, dubbed by us as RfaH-C-terminal domain (RfaH-CTD), and is studied here with a variant of the replica-exchange-with-tunneling approach recently introduced by us. We use the enhanced sampling properties of this technique to map the free-energy landscape of RfaH-CTD and to propose a mechanism for the conversion process.

Comparison of the ability of mammalian eEF1A1 and its oncogenic variant eEF1A2 to interact with actin and calmodulin

The question as to why a protein exerts oncogenic properties is answered mainly by well-established ideas that these proteins interfere with cellular signaling pathways. However, the knowledge about structural and functional peculiarities of the oncoproteins causing these effects is far from comprehensive. The 97.5% homologous tissue-specific A1 and A2 isoforms of mammalian translation elongation factor eEF1A represent an interesting model to study a difference between protein variants of a family that differ in oncogenic potential. We propose that the different oncogenic impact of A1 and A2 might be explained by differences in their ability to communicate with their respective cellular partners. Here we probed this hypothesis by studying the interaction of eEF1A with two known partners - calmodulin and actin. Indeed, an inability of the A2 isoform to interact with calmodulin is shown, while calmodulin is capable of binding A1 and interferes with its tRNA-binding and actin-bundling activities in vitro. Both A1 and A2 variants revealed actin-bundling activity; however, the form of bundles formed in the presence of A1 or A2 was distinctly different. Thus, a potential inability of A2 to be controlled by Ca2+-mediated regulatory systems is revealed.

The Role of Evolutionary Selection in the Dynamics of Protein Structure Evolution

Homology modeling is a powerful tool for predicting a protein's structure. This approach is successful because proteins whose sequences are only 30% identical still adopt the same structure, while structure similarity rapidly deteriorates beyond the 30% threshold. By studying the divergence of protein structure as sequence evolves in real proteins and in evolutionary simulations, we show that this nonlinear sequence-structure relationship emerges as a result of selection for protein folding stability in divergent evolution. Fitness constraints prevent the emergence of unstable protein evolutionary intermediates, thereby enforcing evolutionary paths that preserve protein structure despite broad sequence divergence. However, on longer timescales, evolution is punctuated by rare events where the fitness barriers obstructing structure evolution are overcome and discovery of new structures occurs. We outline biophysical and evolutionary rationale for broad variation in protein family sizes, prevalence of compact structures among ancient proteins, and more rapid structure evolution of proteins with lower packing density.

The Dynameomics Entropy Dictionary: A Large-Scale Assessment of Conformational Entropy across Protein Fold Space

Molecular dynamics (MD) simulations contain considerable information with regard to the motions and fluctuations of a protein, the magnitude of which can be used to estimate conformational entropy. Here we survey conformational entropy across protein fold space using the Dynameomics database, which represents the largest existing data set of protein MD simulations for representatives of essentially all known protein folds. We provide an overview of MD-derived entropies accounting for all possible degrees of dihedral freedom on an unprecedented scale. Although different side chains might be expected to impose varying restrictions on the conformational space that the backbone can sample, we found that the backbone entropy and side chain size are not strictly coupled. An outcome of these analyses is the Dynameomics Entropy Dictionary, the contents of which have been compared with entropies derived by other theoretical approaches and experiment. As might be expected, the conformational entropies scale linearly with the number of residues, demonstrating that conformational entropy is an extensive property of proteins. The calculated conformational entropies of folding agree well with previous estimates. Detailed analysis of specific cases identifies deviations in conformational entropy from the average values that highlight how conformational entropy varies with sequence, secondary structure, and tertiary fold. Notably, α-helices have lower entropy on average than do β-sheets, and both are lower than coil regions.

Distribution and evolution of stable single α-helices (SAH domains) in myosin motor proteins

Stable single-alpha helices (SAHs) are versatile structural elements in many prokaryotic and eukaryotic proteins acting as semi-flexible linkers and constant force springs. This way SAH-domains function as part of the lever of many different myosins. Canonical myosin levers consist of one or several IQ-motifs to which light chains such as calmodulin bind. SAH-domains provide flexibility in length and stiffness to the myosin levers, and may be particularly suited for myosins working in crowded cellular environments. Although the function of the SAH-domains in human class-6 and class-10 myosins has well been characterised, the distribution of the SAH-domain in all myosin subfamilies and across the eukaryotic tree of life remained elusive. Here, we analysed the largest available myosin sequence dataset consisting of 7919 manually annotated myosin sequences from 938 species representing all major eukaryotic branches using the SAH-prediction algorithm of Waggawagga, a recently developed tool for the identification of SAH-domains. With this approach we identified SAH-domains in more than one third of the supposed 79 myosin subfamilies. Depending on the myosin class, the presence of SAH-domains can range from a few to almost all class members indicating complex patterns of independent and taxon-specific SAH-domain gain and loss.

Nucleic and Amino Acid Sequences Support Structure-Based Viral Classification

Viral capsids ensure viral genome integrity by protecting the enclosed nucleic acids. Interactions between the genome and capsid and between individual capsid proteins (i.e., capsid architecture) are intimate and are expected to be characterized by strong evolutionary conservation. For this reason, a capsid structure-based viral classification has been proposed as a way to bring order to the viral universe. The seeming lack of sufficient sequence similarity to reproduce this classification has made it difficult to reject structural convergence as the basis for the classification. We reinvestigate whether the structure-based classification for viral coat proteins making icosahedral virus capsids is in fact supported by previously undetected sequence similarity. Since codon choices can influence nascent protein folding cotranslationally, we searched for both amino acid and nucleotide sequence similarity. To demonstrate the sensitivity of the approach, we identify a candidate gene for the pandoravirus capsid protein. We show that the structure-based classification is strongly supported by amino acid and also nucleotide sequence similarities, suggesting that the similarities are due to common descent. The correspondence between structure-based and sequence-based analyses of the same proteins shown here allow them to be used in future analyses of the relationship between linear sequence information and macromolecular function, as well as between linear sequence and protein folds.

IMPORTANCE Viral capsids protect nucleic acid genomes, which in turn encode capsid proteins. This tight coupling of protein shell and nucleic acids, together with strong functional constraints on capsid protein folding and architecture, leads to the hypothesis that capsid protein-coding nucleotide sequences may retain signatures of ancient viral evolution. We have been able to show that this is indeed the case, using the major capsid proteins of viruses forming icosahedral capsids. Importantly, we detected similarity at the nucleotide level between capsid protein-coding regions from viruses infecting cells belonging to all three domains of life, reproducing a previously established structure-based classification of icosahedral viral capsids.

The GOR Method of Protein Secondary Structure Prediction and Its Application as a Protein Aggregation Prediction Tool

The GOR method of protein secondary structure prediction is described. The original method was published by Garnier, Osguthorpe, and Robson in 1978 and was one of the first successful methods to predict protein secondary structure from amino acid sequence. The method is based on information theory, and an assumption that information function of a protein chain can be approximated by a sum of information from single residues and pairs of residues. The analysis of frequencies of occurrence of secondary structure for singlets and doublets of residues in a protein database enables prediction of secondary structure for new amino acid sequences. Because of these simple physical assumptions the GOR method has a conceptual advantage over other later developed methods such as PHD, PSIPRED, and others that are based on Machine Learning methods (like Neural Networks), give slightly better predictions, but have a "black box" nature. The GOR method has been continuously improved and modified for 30 years with the last GOR V version published in 2002, and the GOR V server developed in 2005. We discuss here the original GOR method and the GOR V program and the web server. Additionally we discuss new highly interesting and important applications of the GOR method to chameleon sequences in protein folding simulations, and for prediction of protein aggregation propensities. Our preliminary studies show that the GOR method is a promising and efficient alternative to other protein aggregation predicting tools. This shows that the GOR method despite being almost 40 years old is still important and has significant potential in application to new scientific problems.

Simulating Protein Fold Switching by Replica Exchange with Tunneling

Recent experiments suggest that an amino acid sequence encodes not only the native fold of a protein but also other forms that are essential for its function or are important during folding or association. These various forms populate a multifunnel folding and association landscape where mutations, changes in environment, or interaction with other molecules switch between the encoded folds. We introduce replica exchange with tunneling as a way to efficiently simulate switching between distinct folds of proteins and protein aggregates. The correctness and efficiency of our approach are demonstrated in a series of simulations covering a wide range of proteins, from a small 11-residue large designed peptide to two 56-residue large mutants of the A and B domains of protein G.

Protein secondary structure prediction using a small training set (compact model) combined with a Complex-valued neural network approach

BACKGROUND

Protein secondary structure prediction (SSP) has been an area of intense research interest. Despite advances in recent methods conducted on large datasets, the estimated upper limit accuracy is yet to be reached. Since the predictions of SSP methods are applied as input to higher-level structure prediction pipelines, even small errors may have large perturbations in final models. Previous works relied on cross validation as an estimate of classifier accuracy. However, training on large numbers of protein chains compromises the classifier ability to generalize to new sequences. This prompts a novel approach to training and an investigation into the possible structural factors that lead to poor predictions. Here, a small group of 55 proteins termed the compact model is selected from the CB513 dataset using a heuristics-based approach. In a prior work, all sequences were represented as probability matrices of residues adopting each of Helix, Sheet and Coil states, based on energy calculations using the C-Alpha, C-Beta, Side-chain (CABS) algorithm. The functional relationship between the conformational energies computed with CABS force-field and residue states is approximated using a classifier termed the Fully Complex-valued Relaxation Network (FCRN). The FCRN is trained with the compact model proteins.

RESULTS

The performance of the compact model is compared with traditional cross-validated accuracies and blind-tested on a dataset of G Switch proteins, obtaining accuracies of ∼81 %. The model demonstrates better results when compared to several techniques in the literature. A comparative case study of the worst performing chain identifies hydrogen bond contacts that lead to Coil ⇔ Sheet misclassifications. Overall, mispredicted Coil residues have a higher propensity to participate in backbone hydrogen bonding than correctly predicted Coils.

CONCLUSIONS

The implications of these findings are: (i) the choice of training proteins is important in preserving the generalization of a classifier to predict new sequences accurately and (ii) SSP techniques sensitive in distinguishing between backbone hydrogen bonding and side-chain or water-mediated hydrogen bonding might be needed in the reduction of Coil ⇔ Sheet misclassifications.

Molecular dynamics simulations on the conformational transitions from the GA 98 (GA 88) to GB 98 (GB 88) proteins

We performed conventional and targeted molecular dynamics simulations to address the dynamic transition mechanisms of the conformational transitions from the G_A 98 protein with only 1 mutation of Leu45Tyr to G_B 98 and from the G_A 88 protein with 7 mutations of Gly24Ala, Ile25Thr, Ile30Phe, Ile33Tyr, Leu45Tyr, Ile49Thr, and Leu50Lys to G_B 88. The results show that the conformational transition mechanism from the mutated 3α G_A 98 (G_A 88) state to the α+4β G_B 98 (G_B 88) state via several intermediate conformations involves the bending of loops at the N and C termini firstly, the unfolding of αA and αC, then the traversing of αB, and the formation of the 4β layer with the conversion of the hydrophobic core. The bending of loops at the N and C termini and the formation of the crucial transition conformation with the full unfolded structure are key factors in their transition processes. The communication of the interaction network, the bending directions of loops, and the traversing site of αB in the transition of G_A 98 to G_B 98 are markedly different from those in G_A 88 to G_B 88 because of the different mutated residues. The analysis of the correlations and the calculated mass center distances between some segments further supported their conformational transition mechanisms. These results could help people to better understand the Paracelsus challenge. Copyright © 2016 John Wiley & Sons, Ltd.

Theoretical Insights into the Biophysics of Protein Bi-stability and Evolutionary Switches

Deciphering the effects of nonsynonymous mutations on protein structure is central to many areas of biomedical research and is of fundamental importance to the study of molecular evolution. Much of the investigation of protein evolution has focused on mutations that leave a protein’s folded structure essentially unchanged. However, to evolve novel folds of proteins, mutations that lead to large conformational modifications have to be involved. Unraveling the basic biophysics of such mutations is a challenge to theory, especially when only one or two amino acid substitutions cause a large-scale conformational switch. Among the few such mutational switches identified experimentally, the one between the G_A all-α and G_B α+β folds is extensively characterized; but all-atom simulations using fully transferrable potentials have not been able to account for this striking switching behavior. Here we introduce an explicit-chain model that combines structure-based native biases for multiple alternative structures with a general physical atomic force field, and apply this construct to twelve mutants spanning the sequence variation between G_A and G_B. In agreement with experiment, we observe conformational switching from G_A to G_B upon a single L45Y substitution in the GA98 mutant. In line with the latent evolutionary potential concept, our model shows a gradual sequence-dependent change in fold preference in the mutants before this switch. Our analysis also indicates that a sharp G_A/G_B switch may arise from the orientation dependence of aromatic π-interactions. These findings provide physical insights toward rationalizing, predicting and designing evolutionary conformational switches.

The biological functions of globular proteins are intimately related to their folded structures and their associated conformational fluctuations. Evolution of new structures is an important avenue to new functions. Although many mutations do not change the folded state, experiments indicate that a single amino acid substitution can lead to a drastic change in the folded structure. The physics of this switch-like behavior remains to be elucidated. Here we develop a computational model for the relevant physical forces, showing that mutations can lead to new folds by passing through intermediate sequences where the old and new folds occur with varying probabilities. Our approach helps provide a general physical account of conformational switching in evolution and mutational effects on conformational dynamics.

Protein sequence design and its applications

Design of proteins has far-reaching potentials in diverse areas that span repurposing of the protein scaffold for reactions and substrates that they were not naturally meant for, to catching a glimpse of the ephemeral proteins that nature might have sampled during evolution. These non-natural proteins, either in synthesized or virtual form have opened the scope for the design of entities that not only rival their natural counterparts but also offer a chance to visualize the protein space continuum that might help to relate proteins and understand their associations. Here, we review the recent advances in protein engineering and design, in multiple areas, with a view to drawing attention to their future potential.

Comprehensive analysis of sequences of a protein switch

Switches form a special class of proteins that dramatically change their three-dimensional structures upon a small perturbation. One possible perturbation that we explore is that of a single point mutation. Building on the pioneering experimental work of Alexander et al. (Alexander et al. PNAS, 2007; 104,11963-11968) that determines switch sequences between α and α+β folds we conduct a comprehensive sequence sampling by a Markov Chain with multiple fitness criteria to identify new switches given the experimental folds. We screen for switch sequences using a combination of contact potential, secondary structure prediction, and finally molecular dynamics simulations. Statistical properties of switch sequences are discussed and illustrated to be most sensitive to mutation at the N- and C- termini of the switch protein. Based on this analysis, a particularly stable putative switch pair is identified and proposed for further experimental analysis.