Protein structure comparison: implications for the nature of 'fold space', and structure and function prediction

The identification of geometric relationships between protein structures offers a powerful approach to predicting the structure and function of proteins. Methods to detect such relationships range from human pattern recognition to a variety of mathematical algorithms. A number of schemes for the classification of protein structure have found widespread use and these implicitly assume the organization of protein structure space into discrete categories. Recently, an alternative view has emerged in which protein fold space is seen as continuous and multidimensional. Significant relationships have been observed between proteins that belong to what have been termed different 'folds'. There has been progress in the use of these relationships in the prediction of protein structure and function.     

Uncovering network systems within protein structures

Traditionally, proteins have been viewed as a construct based on elements of secondary structure and their arrangement in three-dimensional space. In a departure from this perspective we show that protein structures can be modelled as network systems that exhibit small-world, single-scale, and to some degree, scale-free properties. The phenomenological network concept of degrees of separation is applied to three-dimensional protein structure networks and reveals how amino acid residues can be connected to each other within six degrees of separation. This work also illuminates the unique features of protein networks in comparison to other networks currently studied. Recognising that proteins are networks provides a means of rationalising the robustness in the overall three-dimensional fold of a protein against random mutations and suggests an alternative avenue to investigate the determinants of protein structure, function and folding.     

Structural classification of thioredoxin-like fold proteins

Protein structure classification is necessary to comprehend the rapidly growing structural data for better understanding of protein evolution and sequence-structure-function relationships. Thioredoxins are important proteins that ubiquitously regulate cellular redox status and various other crucial functions. We define the thioredoxin-like fold using the structure consensus of thioredoxin homologs and consider all circular permutations of the fold. The search for thioredoxin-like fold proteins in the PDB database identified 723 protein domains. These domains are grouped into eleven evolutionary families based on combined sequence, structural, and functional evidence. Analysis of the protein-ligand structure complexes reveals two major active site locations for the thioredoxin-like proteins. Comparison to existing structure classifications reveals that our thioredoxin-like fold group is broader and more inclusive, unifying proteins from five SCOP folds, five CATH topologies and seven DALI domain dictionary globular folding topologies. Considering these structurally similar domains together sheds new light on the relationships between sequence, structure, function and evolution of thioredoxins.     

Relationship between energy distribution and fold stability: Insights from molecular dynamics simulations of native and mutant proteins

Most proteins must fold to a well-defined structure with a minimal stability to perform their function. Here we use a simple, molecular dynamics-based, energy decomposition approach to map the principal energetic interactions in a set of proteins representative of different folds. This work involves the all-atom simulation and analysis of the native structures and mutants of five different proteins representative of an all-alpha (yACPB, Protein A), all-beta (SH3), and a mixed alpha/beta fold (Proteins G and L). Given a certain structure, a native sequence and a set of mutants, we show that our model discriminates the ability of a mutation to yield a more or less stable protein, in agreement with experimental data, catching the principal energetic determinants of protein stabilization. Our approach identifies the interaction determinants responsible to define a fold and shows that mutations can either modulate the strength of pair-wise coupling between residues important for folding, or modify the profile of the principal interactions. Furthermore, we address the question of how to evaluate the fitness of a sequence to a given structure by comparing the information contained in the energy map, which recapitulates the chemistry of the sequence, to that contained in the contact map, which recapitulates the fold topology. The results show that the better fit between the energetic properties of the sequence and the fold topology corresponds to a higher stabilization of the protein. We discuss the relevance of these observations to the analysis of protein designability and to the rational evolution of new sequences.     

Understanding protein non-folding

This review describes the family of intrinsically disordered proteins, members of which fail to form rigid 3-D structures under physiological conditions, either along their entire lengths or only in localized regions. Instead, these intriguing proteins/regions exist as dynamic ensembles within which atom positions and backbone Ramachandran angles exhibit extreme temporal fluctuations without specific equilibrium values. Many of these intrinsically disordered proteins are known to carry out important biological functions which, in fact, depend on the absence of a specific 3-D structure. The existence of such proteins does not fit the prevailing structure–function paradigm, which states that a unique 3-D structure is a prerequisite to function. Thus, the protein structure–function paradigm has to be expanded to include intrinsically disordered proteins and alternative relationships among protein sequence, structure, and function. This shift in the paradigm represents a major breakthrough for biochemistry, biophysics and molecular biology, as it opens new levels of understanding with regard to the complex life of proteins. This review will try to answer the following questions: how were intrinsically disordered proteins discovered? Why don't these proteins fold? What is so special about intrinsic disorder? What are the functional advantages of disordered proteins/regions? What is the functional repertoire of these proteins? What are the relationships between intrinsically disordered proteins and human diseases?     

A galaxy of folds

Many protein classification systems capture homologous relationships by grouping domains into families and superfamilies on the basis of sequence similarity. Superfamilies with similar 3D structures are further grouped into folds. In the absence of discernable sequence similarity, these structural similarities were long thought to have originated independently, by convergent evolution. However, the growth of databases and advances in sequence comparison methods have led to the discovery of many distant evolutionary relationships that transcend the boundaries of superfamilies and folds. To investigate the contributions of convergent versus divergent evolution in the origin of protein folds, we clustered representative domains of known structure by their sequence similarity, treating them as point masses in a virtual 2D space which attract or repel each other depending on their pairwise sequence similarities. As expected, families in the same superfamily form tight clusters. But often, superfamilies of the same fold are linked with each other, suggesting that the entire fold evolved from an ancient prototype. Strikingly, some links connect superfamilies with different folds. They arise from modular peptide fragments of between 20 and 40 residues that co‐occur in the connected folds in disparate structural contexts. These may be descendants of an ancestral pool of peptide modules that evolved as cofactors in the RNA world and from which the first folded proteins arose by amplification and recombination. Our galaxy of folds summarizes, in a single image, most known and many yet undescribed homologous relationships between protein superfamilies, providing new insights into the evolution of protein domains.     

Homology versus analogy: possible evolutionary relationship of immunoglobulins, cupredoxins, and Cu,Zn-superoxide dismutase

The 'immunoglobulin-like' fold is one of most common structural motifs observed in proteins. This topology is found in more than 80 superfamilies of proteins, including Cu,Zn-superoxide dismutase (SOD) and cupredoxin. Evolutionary relationships have not been identified, but may exist. The challenge remains, therefore, of resolving the issue of whether the diverse distribution of the fold is accounted for by divergent evolution of function or convergent evolution of structure following multiple independent origins of function. Since the early studies that revealed conformational similarity of immunoglobulins and other proteins, the number of primary structures available for comparison has dramatically increased and new computational approaches for analysis of sequences have been developed. It now appears that a hypothesis of a common evolutionary origin for cupredoxins, Cu,Zn-SOD, and immunoglobulins may be credible. The distinction between protein homology and protein analogy is fundamental. The immunoglobulin-like fold may represent a robust system within which to examine again the issue of protein homology versus analogy.     

A novel member of the YchN-like fold: solution structure of the hypothetical protein Tm0979 from Thermotoga maritima

We report herein the NMR structure of Tm0979, a structural proteomics target from Thermotoga maritima. The Tm0979 fold consists of four beta/alpha units, which form a central parallel beta-sheet with strand order 1234. The first three helices pack toward one face of the sheet and the fourth helix packs against the other face. The protein forms a dimer by adjacent parallel packing of the fourth helices sandwiched between the two beta-sheets. This fold is very interesting from several points of view. First, it represents the first structure determination for the DsrH family of conserved hypothetical proteins, which are involved in oxidation of intracellular sulfur but have no defined molecular function. Based on structure and sequence analysis, possible functions are discussed. Second, the fold of Tm0979 most closely resembles YchN-like folds; however the proteins that adopt these folds differ in secondary structural elements and quaternary structure. Comparison of these proteins provides insight into possible mechanisms of evolution of quaternary structure through a simple mechanism of hydrophobicity-changing mutations of one or two residues. Third, the Tm0979 fold is found to be similar to flavodoxin-like folds and beta/alpha barrel proteins, and may provide a link between these very abundant folds and putative ancestral half-barrel proteins.     

Understanding the Origins of Loss of Protein Function by Analyzing the Effects of Thousands of Variants on Activity and Abundance

Understanding and predicting how amino acid substitutions affect proteins are keys to our basic understanding of protein function and evolution. Amino acid changes may affect protein function in a number of ways including direct perturbations of activity or indirect effects on protein folding and stability. We have analyzed 6,749 experimentally determined variant effects from multiplexed assays on abundance and activity in two proteins (NUDT15 and PTEN) to quantify these effects and find that a third of the variants cause loss of function, and about half of loss-of-function variants also have low cellular abundance. We analyze the structural and mechanistic origins of loss of function and use the experimental data to find residues important for enzymatic activity. We performed computational analyses of protein stability and evolutionary conservation and show how we may predict positions where variants cause loss of activity or abundance. In this way, our results link thermodynamic stability and evolutionary conservation to experimental studies of different properties of protein fitness landscapes.     

Protein folding: a problem with multiple solutions

There is continued interest in predicting the structure of proteins either at the simplest level of identifying their fold class or persevering all the way to an atomic resolution structure. Protein folding methods have become very sophisticated and many successes have been recorded with claims to have solved the native structure of the protein. But for any given protein, there may be more than one solution. Many proteins can exist in one of the other two (or more) different forms and some populate multiple metastable states. Here, the two-state case is considered and the key structural changes that take place when the protein switches from one state to the other are identified. Analysis of these results show that hydrogen bonding patterns and hydrophobic contacts vary considerably between different conformers. Contrary to what has often been assumed previously, these two types of interaction operate essentially independently of one another. Core packing is critical for proper protein structure and function and it is shown that there are considerable changes in internal cavity volumes in many cases. The way in which these switches are made is fold dependent. Considerations such as these need to be taken into account in protein structure prediction.     

Representing and comparing protein folds and fold families using three‐dimensional shape‐density representations

The question of how best to compare and classify the (three‐dimensional) structures of proteins is one of the most important unsolved problems in computational biology. To help tackle this problem, we have developed a novel shape‐density superposition algorithm called 3D‐Blast which represents and superposes the shapes of protein backbone folds using the spherical polar Fourier correlation technique originally developed by us for protein docking. The utility of this approach is compared with several well‐known protein structure alignment algorithms using receiver‐operator‐characteristic plots of queries against the “gold standard” CATH database. Despite being completely independent of protein sequences and using no information about the internal geometry of proteins, our results from searching the CATH database show that 3D‐Blast is highly competitive compared to current state‐of‐the‐art protein structure alignment algorithms. A novel and potentially very useful feature of our approach is that it allows an average or “consensus” fold to be calculated easily for a given group of protein structures. We find that using consensus shapes to represent entire fold families also gives very good database query performance. We propose that using the notion of consensus fold shapes could provide a powerful new way to index existing protein structure databases, and that it offers an objective way to cluster and classify all of the currently known folds in the protein universe. Proteins 2012. © 2011 Wiley Periodicals, Inc.     

蛋白质结构与功能中的结构域

结构域是蛋白质亚基结构中的紧密球状区域.结构域作为蛋白质结构中介于二级与三级结构之间的又一结构层次,在蛋白质中起着独立的结构单位、功能单位与折叠单位的作用.在复杂蛋白质中,结构域具有结构与功能组件与遗传单位的作用.结构域层次的研究将会促进蛋白质结构与功能关系、蛋白质折叠机制以及蛋白质设计的研究.     

De novo protein design using pairwise potentials and a genetic algorithm.

One of the major goals of molecular biology is to understand how protein chains fold into a unique 3-dimensional structure. Given this knowledge, perhaps the most exciting prospect will be the possibility of designing new proteins to perform designated tasks, an application that could prove to be of great importance in medicine and biotechnology. It is possible that effective protein design may be achieved without the requirement for a full understanding of the protein folding process. In this paper a simple method is described for designing an amino acid sequence to fit a given 3-dimensional structure. The compatibility of a designed sequence with a given fold is assessed by means of a set of statistically determined potentials (including interresidue pairwise and solvation terms), which have been previously applied to the problem of protein fold recognition. In order to generate sequences that best fit the fold, a genetic algorithm is used, whereby the sequence is optimized by a stochastic search in the style of natural selection.     

Protein surface analysis for function annotation in high-throughput structural genomics pipeline

Structural genomics (SG) initiatives are expanding the universe of protein fold space by rapidly determining structures of proteins that were intentionally selected on the basis of low sequence similarity to proteins of known structure. Often these proteins have no associated biochemical or cellular functions. The SG success has resulted in an accelerated deposition of novel structures. In some cases the structural bioinformatics analysis applied to these novel structures has provided specific functional assignment. However, this approach has also uncovered limitations in the functional analysis of uncharacterized proteins using traditional sequence and backbone structure methodologies. A novel method, named pvSOAR (pocket and void Surface of Amino Acid Residues), of comparing the protein surfaces of geometrically defined pockets and voids was developed. pvSOAR was able to detect previously unrecognized and novel functional relationships between surface features of proteins. In this study, pvSOAR is applied to several structural genomics proteins. We examined the surfaces of YecM, BioH, and RpiB from Escherichia coli as well as the CBS domains from inosine-5'-monosphate dehydrogenase from Streptococcus pyogenes, conserved hypothetical protein Ta549 from Thermoplasm acidophilum, and CBS domain protein mt1622 from Methanobacterium thermoautotrophicum with the goal to infer information about their biochemical function.     

A comparison of genotype-phenotype maps for RNA and proteins

The relationship between the genotype (sequence) and the phenotype (structure) of macromolecules affects their ability to evolve new structures and functions. We here compare the genotype space organization of proteins and RNA molecules to identify differences that may affect this ability. To this end, we computationally study the genotype-phenotype relationship for short RNA and lattice proteins of a reduced monomer alphabet size, to make exhaustive analysis and direct comparison of their genotype spaces feasible. We find that many fewer protein molecules than RNA molecules fold, but they fold into many more structures than RNA. In consequence, protein phenotypes have smaller genotype networks whose member genotypes tend to be more similar than for RNA phenotypes. Neighborhoods in sequence space of a given radius around an RNA molecule contain more novel structures than for protein molecules. We compare this property to evidence from natural RNA and protein molecules, and conclude that RNA genotype space may be more conducive to the evolution of new structure phenotypes.     

Resolving protein structure‐function‐binding site relationships from a binding site similarity network perspective

Functional annotation is seldom straightforward with complexities arising due to functional divergence in protein families or functional convergence between non‐homologous protein families, leading to mis‐annotations. An enzyme may contain multiple domains and not all domains may be involved in a given function, adding to the complexity in function annotation. To address this, we use binding site information from bound cognate ligands and catalytic residues, since it can help in resolving fold‐function relationships at a finer level and with higher confidence. A comprehensive database of 2,020 fold‐function‐binding site relationships has been systematically generated. A network‐based approach is employed to capture the complexity in these relationships, from which different types of associations are deciphered, that identify versatile protein folds performing diverse functions, same function associated with multiple folds and one‐to‐one relationships. Binding site similarity networks integrated with fold, function, and ligand similarity information are generated to understand the depth of these relationships. Apart from the observed continuity in the functional site space, network properties of these revealed versatile families with topologically different or dissimilar binding sites and structural families that perform very similar functions. As a case study, subtle changes in the active site of a set of evolutionarily related superfamilies are studied using these networks. Tracing of such similarities in evolutionarily related proteins provide clues into the transition and evolution of protein functions. Insights from this study will be helpful in accurate and reliable functional annotations of uncharacterized proteins, poly‐pharmacology, and designing enzymes with new functional capabilities. Proteins 2017; 85:1319–1335. © 2017 Wiley Periodicals, Inc.     

Mutagenic dissection of the sequence determinants of protein folding,recognition, and machine function

Understanding the relationship between the amino‐acid sequence of a protein and its ability to fold and to function is one of the major challenges of protein science. Here, cases are reviewed in which mutagenesis, biochemistry, structure determination, protein engineering, and single‐molecule biophysics have illuminated the sequence determinants of folding, binding specificity, and biological function for DNA‐binding proteins and ATP‐fueled machines that forcibly unfold native proteins as a prelude to degradation. In addition to structure‐function relationships, these studies provide information about folding intermediates, mutations that accelerate folding, slow unfolding, and stabilize proteins against denaturation, show how new binding specificities and folds can evolve, and reveal strategies that proteolytic machines use to recognize, unfold, and degrade thousands of distinct substrates.     

Fold usage on genomes and protein fold evolution

We review fold usage on completed genomes to explore protein structure evolution. The patterns of presence or absence of folds on genomes gives us insights into the relationships between folds, the age of different folds and how we have arrived at the set of folds we see today. We examine the relationships between different measures which describe protein fold usage, such as the number of copies of a fold per genome, the number of families per fold, and the number of genomes a fold occurs on. We obtained these measures of fold usage by searching for the structural domains on 157 completed genome sequences from all three kingdoms of life. In our comparisons of these measures we found that bacteria have relatively more distinct folds on their genomes than archaea. Eukaryotes were found to have many more copies of a fold on their genomes. If we separate out the different fold classes, the alpha/beta class has relatively fewer distinct folds on large genomes, more copies of a fold on bacteria and more folds occurring in all three kingdoms simultaneously. These results possibly indicate that most alpha/beta folds originated earlier than other folds. The expected power law distribution is observed for copies of a fold per genome and we found a similar distribution for the number of families per fold. However, a more complicated distribution appears for fold occurrence across genomes, which strongly depends on fold class and kingdom. We also show that there is not a clear relationship between the three measures of fold usage. A fold which occurs on many genomes does not necessarily have many copies on each genome. Similarly, folds with many copies do not necessarily have many families or vice versa.     

Ribonucleotide reductases: divergent evolution of an ancient enzyme

Ribonucleotide reductases (RNRs) are uniquely responsible for converting nucleotides to deoxynucleotides in all dividing cells. The three known classes of RNRs operate through a free radical mechanism but differ in the way in which the protein radical is generated. Class I enzymes depend on oxygen for radical generation, class II uses adenosylcobalamin, and the anaerobic class III requires S-adenosylmethionine and an iron–sulfur cluster. Despite their metabolic prominence, the evolutionary origin and relationships between these enzymes remain elusive. This gap in RNR knowledge can, to a major extent, be attributed to the fact that different RNR classes exhibit greatly diverged polypeptide chains, rendering homology assessments inconclusive. Evolutionary studies of RNRs conducted until now have focused on comparison of the amino acid sequence of the proteins, without considering how they fold into space. The present study is an attempt to understand the evolutionary history of RNRs taking into account their three-dimensional structure. We first infer the structural alignment by superposing the equivalent stretches of the three-dimensional structures of representatives of each family. We then use the structural alignment to guide the alignment of all publicly available RNR sequences. Our results support the hypothesis that the three RNR classes diverged from a common ancestor currently represented by the anaerobic class III. Also, lateral transfer appears to have played a significant role in the evolution of this protein family.     

Connecting the protein structure universe by using sparse recurring fragments

The quest to order and classify protein structures has lead to various classification schemes, focusing mostly on hierarchical relationships between structural domains. At the coarsest classification level, such schemes typically identify hundreds of types of fundamental units called folds. As a result, we picture protein structure space as a collection of isolated fold islands. It is obvious, however, that many protein folds share structural and functional commonalities. Locating those commonalities is important for our understanding of protein structure, function, and evolution. Here, we present an alternative view of the protein fold space, based on an interfold similarity measure that is related to the frequency of fragments shared between folds. In this view, protein structures form a complicated, crossconnected network with very interesting topology. We show that interfold similarity based on sequence/structure fragments correlates well with similarities of functions between protein populations in different folds.