Structure-based functional inference in structural genomics

The dramatically increasing number of new protein sequences arising from genomics 4 proteomics requires the need for methods to rapidly and reliably infer the molecular and cellular functions of these proteins. One such approach, structural genomics, aims to delineate the total repertoire of protein folds in nature, thereby providing three-dimensional folding patterns for all proteins and to infer molecular functions of the proteins based on the combined information of structures and sequences. The goal of obtaining protein structures on a genomic scale has motivated the development of high throughput technologies and protocols for macromolecular structure determination that have begun to produce structures at a greater rate than previously possible. These new structures have revealed many unexpected functional inferences and evolutionary relationships that were hidden at the sequence level. Here, we present samples of structures determined at Berkeley Structural Genomics Center and collaborators laboratories to illustrate how structural information provides and complements sequence information to deduce the functional inferences of proteins with unknown molecular functions.Two of the major premises of structural genomics are to discover a complete repertoire of protein folds in nature and to find molecular functions of the proteins whose functions are not predicted from sequence comparison alone. To achieve these objectives on a genomic scale, new methods, protocols, and technologies need to be developed by multi-institutional collaborations worldwide. As part of this effort, the Protein Structure Initiative has been launched in the United States (PSI; www.nigms.nih.gov/funding/psi.html). Although infrastructure building and technology development are still the main focus of structural genomics programs [1–6], a considerable number of protein structures have already been produced, some of them coming directly out of semi-automated structure determination pipelines [6–10]. The Berkeley Structural Genomics Center (BSGC) has focused on the proteins of Mycoplasma or their homologues from other organisms as its structural genomics targets because of the minimal genome size of the Mycoplasmas as well as their relevance to human and animal pathogenicity (http://www.strgen.org). Here we present several protein examples encompassing a spectrum of functional inferences obtainable from their three-dimensional structures in five situations, where the inferences are new and testable, and are not predictable from protein sequence information alone.     

Structural genomics is the largest contributor of novel structural leverage

The Protein Structural Initiative (PSI) at the US National Institutes of Health (NIH) is funding four large-scale centers for structural genomics (SG). These centers systematically target many large families without structural coverage, as well as very large families with inadequate structural coverage. Here, we report a few simple metrics that demonstrate how successfully these efforts optimize structural coverage: while the PSI-2 (2005-now) contributed more than 8% of all structures deposited into the PDB, it contributed over 20% of all novel structures (i.e. structures for protein sequences with no structural representative in the PDB on the date of deposition). The structural coverage of the protein universe represented by today’s UniProt (v12.8) has increased linearly from 1992 to 2008; structural genomics has contributed significantly to the maintenance of this growth rate. Success in increasing novel leverage (defined in Liu et al. in Nat Biotechnol 25:849–851, 2007) has resulted from systematic targeting of large families. PSI’s per structure contribution to novel leverage was over 4-fold higher than that for non-PSI structural biology efforts during the past 8 years. If the success of the PSI continues, it may just take another ~15 years to cover most sequences in the current UniProt database.     

Methodologies for target selection in structural genomics

As the number of complete genomes that have been sequenced keeps growing, unknown areas of the protein space are revealed and new horizons open up. Most of this information will be fully appreciated only when the structural information about the encoded proteins becomes available. The goal of structural genomics is to direct large-scale efforts of protein structure determination, so as to increase the impact of these efforts. This review focuses on current approaches in structural genomics aimed at selecting representative proteins as targets for structure determination. We will discuss the concept of representative structures/folds, the current methodologies for identifying those proteins, and computational techniques for identifying proteins which are expected to adopt new structural folds.     

A short survey on protein blocks

Protein structures are classically described in terms of secondary structures. Even if the regular secondary structures have relevant physical meaning, their recognition from atomic coordinates has some important limitations such as uncertainties in the assignment of boundaries of helical and β-strand regions. Further, on an average about 50% of all residues are assigned to an irregular state, i.e., the coil. Thus different research teams have focused on abstracting conformation of protein backbone in the localized short stretches. Using different geometric measures, local stretches in protein structures are clustered in a chosen number of states. A prototype representative of the local structures in each cluster is generally defined. These libraries of local structures prototypes are named as "structural alphabets". We have developed a structural alphabet, named Protein Blocks, not only to approximate the protein structure, but also to predict them from sequence. Since its development, we and other teams have explored numerous new research fields using this structural alphabet. We review here some of the most interesting applications.     

The opportunities and challenges posed by the new generation of deep learning-based protein structure predictors

The function of proteins can often be inferred from their three-dimensional structures. Experimental structural biologists spent decades studying these structures, but the accelerated pace of protein sequencing continuously increases the gaps between sequences and structures. The early 2020s saw the advent of a new generation of deep learning-based protein structure prediction tools that offer the potential to predict structures based on any number of protein sequences.In this review, we give an overview of the impact of this new generation of structure prediction tools, with examples of the impacted field in the life sciences. We discuss the novel opportunities and new scientific and technical challenges these tools present to the broader scientific community. Finally, we highlight some potential directions for the future of computational protein structure prediction.     

Progressive structure-based alignment of homologous proteins: Adopting sequence comparison strategies

Comparison of multiple protein structures has a broad range of applications in the analysis of protein structure, function and evolution. Multiple structure alignment tools (MSTAs) are necessary to obtain a simultaneous comparison of a family of related folds. In this study, we have developed a method for multiple structure comparison largely based on sequence alignment techniques. A widely used Structural Alphabet named Protein Blocks (PBs) was used to transform the information on 3D protein backbone conformation as a 1D sequence string. A progressive alignment strategy similar to CLUSTALW was adopted for multiple PB sequence alignment (mulPBA). Highly similar stretches identified by the pairwise alignments are given higher weights during the alignment. The residue equivalences from PB based alignments are used to obtain a three dimensional fit of the structures followed by an iterative refinement of the structural superposition. Systematic comparisons using benchmark datasets of MSTAs underlines that the alignment quality is better than MULTIPROT, MUSTANG and the alignments in HOMSTRAD, in more than 85% of the cases. Comparison with other rigid-body and flexible MSTAs also indicate that mulPBA alignments are superior to most of the rigid-body MSTAs and highly comparable to the flexible alignment methods.     

Strategies for high-throughput comparative modeling: applications to leverage analysis in structural genomics and protein family organization

The technological breakthroughs in structural genomics were designed to facilitate the solution of a sufficient number of structures, so that as many protein sequences as possible can be structurally characterized with the aid of comparative modeling. The leverage of a solved structure is the number and quality of the models that can be produced using the structure as a template for modeling and may be viewed as the "currency" with which the success of a structural genomics endeavor can be measured. Moreover, the models obtained in this way should be valuable to all biologists. To this end, at the Northeast Structural Genomics Consortium (NESG), a modular computational pipeline for automated high-throughput leverage analysis was devised and used to assess the leverage of the 186 unique NESG structures solved during the first phase of the Protein Structure Initiative (January 2000 to July 2005). Here, the results of this analysis are presented. The number of sequences in the nonredundant protein sequence database covered by quality models produced by the pipeline is approximately 39,000, so that the average leverage is approximately 210 models per structure. Interestingly, only 7900 of these models fulfill the stringent modeling criterion of being at least 30% sequence-identical to the corresponding NESG structures. This study shows how high-throughput modeling increases the efficiency of structure determination efforts by providing enhanced coverage of protein structure space. In addition, the approach is useful in refining the boundaries of structural domains within larger protein sequences, subclassifying sequence diverse protein families, and defining structure-based strategies specific to a particular family.     

About the use of protein models

Protein models can be of great assistance in functional genomics, as they provide the structural insights often necessary to understand protein function. Although comparative modelling is far from yielding perfect structures, this is still the most reliable method and the quality of the predictions is now well understood. Models can be classified according to their correctness and accuracy, which will impact their applicability and usefulness in functional genomics and a variety of situations.     

Predicted role for the archease protein family based on structural and sequence analysis of TM1083 and MTH1598, two proteins structurally characterized through structural genomics efforts

Recently, the structures of two proteins belonging to the archease family, TM1083 from Thermotoga maritima and MTH1598 from Methanobacterium thermoautotrophicum, have been solved independently by two Protein Structure Initiative structural genomics pilot centers using X-ray crystallography and NMR, respectively. The archease protein family is a good example of one of the paradoxes of structural genomics: Approximately one third of protein structures produced by structural genomics centers have no known function and are still annotated as "hypothetical proteins" in the Protein Data Bank. In the case of archeases, despite the existence of two protein structures and abundant sequence information, there is still no function assigned to this protein family. Here, our group predicts, based on structural similarity, sequence conservation, and gene context analyses, that members of this protein family might function as chaperones or modulators of proteins involved in DNA/RNA processing. The conservation of genomic context for this protein family is constant from Archaea and Bacteria to humans, and suggests that unannotated open reading frames contiguous to them could be novel RNA/DNA binding proteins.     

Structural analysis of a set of proteins resulting from a bacterial genomics project

The targets of the Structural GenomiX (SGX) bacterial genomics project were proteins conserved in multiple prokaryotic organisms with no obvious sequence homolog in the Protein Data Bank of known structures. The outcome of this work was 80 structures, covering 60 unique sequences and 49 different genes. Experimental phase determination from proteins incorporating Se-Met was carried out for 45 structures with most of the remainder solved by molecular replacement using members of the experimentally phased set as search models. An automated tool was developed to deposit these structures in the Protein Data Bank, along with the associated X-ray diffraction data (including refined experimental phases) and experimentally confirmed sequences. BLAST comparisons of the SGX structures with structures that had appeared in the Protein Data Bank over the intervening 3.5 years since the SGX target list had been compiled identified homologs for 49 of the 60 unique sequences represented by the SGX structures. This result indicates that, for bacterial structures that are relatively easy to express, purify, and crystallize, the structural coverage of gene space is proceeding rapidly. More distant sequence-structure relationships between the SGX and PDB structures were investigated using PDB-BLAST and Combinatorial Extension (CE). Only one structure, SufD, has a truly unique topology compared to all folds in the PDB.     

Effect of low-complexity regions on protein structure determination

It has been previously shown that protein sequences containing a quasi-repetitive assortment of amino acids are common in genomes and databases such as Swiss-Prot but are under-represented in the structure-based Protein Data Bank (PDB). Structural genomics groups have been using the absence of these “low-complexity” sequences for several years as a way to select proteins that have a good chance of successful structure determination. In this study, we examine the data deposited in the PDB as well as the available data from structural genomics groups in TargetDB and PepcDB to reveal interesting trends that could be taken into consideration when using low-complexity sequences as part of the target selection process.     

Machine learning in protein structure prediction

Prediction of protein structure from sequence has been intensely studied for many decades, owing to the problem's importance and its uniquely well-defined physical and computational bases. While progress has historically ebbed and flowed, the past two years saw dramatic advances driven by the increasing “neuralization” of structure prediction pipelines, whereby computations previously based on energy models and sampling procedures are replaced by neural networks. The extraction of physical contacts from the evolutionary record; the distillation of sequence–structure patterns from known structures; the incorporation of templates from homologs in the Protein Databank; and the refinement of coarsely predicted structures into finely resolved ones have all been reformulated using neural networks. Cumulatively, this transformation has resulted in algorithms that can now predict single protein domains with a median accuracy of 2.1 Å, setting the stage for a foundational reconfiguration of the role of biomolecular modeling within the life sciences.     

Structural genomics—Impact on biomedicine and drug discovery

The field of structural genomics emerged as one of many 'omics disciplines more than a decade ago, and a multitude of large scale initiatives have been launched across the world. Development and implementation of methods for high-throughput structural biology represents a common denominator among different structural genomics programs. From another perspective a distinction between “biology-driven” versus “structure-driven” approaches can be made. This review outlines the general themes of structural genomics, its achievements and its impact on biomedicine and drug discovery. The growing number of high resolution structures of known and potential drug target proteins is expected to have tremendous value for future drug discovery programs. Moreover, the availability of large numbers of purified proteins enables generation of tool reagents, such as chemical probes and antibodies, to further explore protein function in the cell.     

Discrete analyses of protein dynamics

Abstract

Protein structures are highly dynamic macromolecules. This dynamics is often analysed through experimental and/or computational methods only for an isolated or a limited number of proteins. Here, we explore large-scale protein dynamics simulation to observe dynamics of local protein conformations using different perspectives. We analysed molecular dynamics to investigate protein flexibility locally, using classical approaches such as RMSf, solvent accessibility, but also innovative approaches such as local entropy. First, we focussed on classical secondary structures and analysed specifically how β-strand, β–turns, and bends evolve during molecular simulations. We underlined interesting specific bias between β–turns and bends, which are considered as the same category, while their dynamics show differences. Second, we used a structural alphabet that is able to approximate every part of the protein structures conformations, namely protein blocks (PBs) to analyse (i) how each initial local protein conformations evolve during dynamics and (ii) if some exchange can exist among these PBs. Interestingly, the results are largely complex than simple regular/rigid and coil/flexible exchange. Abbreviations N_eq number of equivalent

PB Protein Blocks

PDB Protein DataBank

RMSf root mean square fluctuations

Communicated by Ramaswamy H. Sarma     

Quantifying protein modularity and evolvability: A comparison of different techniques

Modularity increases evolvability by reducing constraints on adaptation and by allowing preexisting parts to function in new contexts for novel uses. Protein evolution provides an excellent context to study the causes and consequences of biological modularity. In order to address such questions, however, an index for protein modularity is necessary. This paper proposes a simple index for protein modularity-"module density"-which is the number of evolutionarily independent modules that compose a protein divided by the number of amino acids in the protein. The decomposition of proteins into constituent modules can be accomplished by either of two classes of methods. The first class of methods relies on "suppositional" criteria to assign amino acids to modules, whereas the second class of methods relies on "coevolutionary" criteria for this task. One simple and practical method from the first class consists of approximating the number of modules in a protein as the number of regular secondary structure elements (i.e., helices and sheets). Methods based on coevolutionary criteria require more elaborate data, but they have the advantage of being able to specify modules without prior assumptions about why they exist. Given the increasing availability of datasets sampling protein mutational spectra (e.g., from comparative genomics, experimental evolution, and computational prediction), methods based on coevolutionary criteria will likely become more promising in the near future. The ability to meaningfully quantify protein modularity via simple indices has the potential to aid future efforts to understand protein evolutionary rate determinants, improve molecular evolution models and engineer novel proteins.     

Assisted assignment of ligands corresponding to unknown electron density

A semi-automated computational procedure to assist in the identification of bound ligands from unknown electron density has been developed. The atomic surface surrounding the density blob is compared to a library of three-dimensional ligand binding surfaces extracted from the Protein Data Bank (PDB). Ligands corresponding to surfaces which share physicochemical texture and geometric shape similarities are considered for assignment. The method is benchmarked against a set of well represented ligands from the PDB, in which we show that we can identify the correct ligand based on the corresponding binding surface. Finally, we apply the method during model building and refinement stages from structural genomics targets in which unknown density blobs were discovered. A semi-automated computational method is described which aims to assist crystallographers with assigning the identity of a ligand corresponding to unknown electron density. Using shape and physicochemical similarity assessments between the protein surface surrounding the density and a database of known ligand binding surfaces, a plausible list of candidate ligands are identified for consideration. The method is validated against highly observed ligands from the Protein Data Bank and results are shown from its use in a high-throughput structural genomics pipeline.