Focusing in on structural genomics: the University of Queensland structural biology pipeline

The flood of new genomic sequence information together with technological innovations in protein structure determination have led to worldwide structural genomics (SG) initiatives. The goals of SG initiatives are to accelerate the process of protein structure determination, to fill in protein fold space and to provide information about the function of uncharacterized proteins. In the long-term, these outcomes are likely to impact on medical biotechnology and drug discovery, leading to a better understanding of disease as well as the development of new therapeutics. Here we describe the high throughput pipeline established at the University of Queensland in Australia. In this focused pipeline, the targets for structure determination are proteins that are expressed in mouse macrophage cells and that are inferred to have a role in innate immunity. The aim is to characterize the molecular structure and the biochemical and cellular function of these targets by using a parallel processing pipeline. The pipeline is designed to work with tens to hundreds of target gene products and comprises target selection, cloning, expression, purification, crystallization and structure determination. The structures from this pipeline will provide insights into the function of previously uncharacterized macrophage proteins and could lead to the validation of new drug targets for chronic obstructive pulmonary disease and arthritis.     

Methods and Results for Semi-automated Cloning Using Integrated Robotics

The Joint Center for Structural Genomics (JCSG) has emphasized automation and parallel processing approaches. Here, we describe automated methods used across the cloning process with results from JCSG projects. The protocols for PCR, restriction digests and ligations, as well as for gel electrophoresis and microtiter plate assays have all been automated. The system has the capacity to routinely process 384 clones a week. This throughput can adequately supply our expression and purification pipeline with expression-ready clones, including novel targets and truncations. The utility of our system is demonstrated by our results from three diverse projects. In summary, 94% of the PCR amplicons generated to date have been successfully cloned and verified by sequencing (83% of the total attempted targets). Our results demonstrate the capabilities of this robotic platform to provide an avenue to high-throughput cloning which requires little manpower and is rapid and cost-effective while providing insights for method optimization.     

Performance and enhancement of the LZerD protein assembly pipeline in CAPRI 38-46

We report the performance of the protein docking prediction pipeline of our group and the results for Critical Assessment of Prediction of Interactions (CAPRI) rounds 38-46. The pipeline integrates programs developed in our group as well as other existing scoring functions. The core of the pipeline is the LZerD protein-protein docking algorithm. If templates of the target complex are not found in PDB, the first step of our docking prediction pipeline is to run LZerD for a query protein pair. Meanwhile, in the case of human group prediction, we survey the literature to find information that can guide the modeling, such as protein-protein interface information. In addition to any literature information and binding residue prediction, generated docking decoys were selected by a rank aggregation of statistical scoring functions. The top 10 decoys were relaxed by a short molecular dynamics simulation before submission to remove atom clashes and improve side-chain conformations. In these CAPRI rounds, our group, particularly the LZerD server, showed robust performance. On the other hand, there are failed cases where some other groups were successful. To understand weaknesses of our pipeline, we analyzed sources of errors for failed targets. Since we noted that structure refinement is a step that needs improvement, we newly performed a comparative study of several refinement approaches. Finally, we show several examples that illustrate successful and unsuccessful cases by our group.     

Ratiocinative screen of eukaryotic integral membrane protein expression and solubilization for structure determination

Persistent hurdles impede the successful determination of high-resolution crystal structures of eukaryotic integral membrane proteins (IMP). We designed a high-throughput structural genomics oriented pipeline that seeks to minimize effort in uncovering high-quality, responsive non-redundant targets for crystallization. This “discovery-oriented” pipeline sidesteps two significant bottlenecks in the IMP structure determination pipeline: expression and membrane extraction with detergent. In addition, proteins that enter the pipeline are then rapidly vetted by their presence in the included volume on a size-exclusion column—a hallmark of well-behaved IMP targets. A screen of 384 rationally selected eukaryotic IMPs in baker’s yeast Saccharomyces cerevisiae is outlined to demonstrate the results expected when applying this discovery-oriented pipeline to whole-organism membrane proteomes. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users. Franklin A. Hays and Zygy Roe-Zurz have contributed equally to this work.     

The MASH pipeline for protein function prediction and an algorithm for the geometric refinement of 3D motifs.

The development of new and effective drugs is strongly affected by the need to identify drug targets and to reduce side effects. Resolving these issues depends partially on a thorough understanding of the biological function of proteins. Unfortunately, the experimental determination of protein function is expensive and time consuming. To support and accelerate the determination of protein functions, algorithms for function prediction are designed to gather evidence indicating functional similarity with well studied proteins. One such approach is the MASH pipeline, described in the first half of this paper. MASH identifies matches of geometric and chemical similarity between motifs, representing known functional sites, and substructures of functionally uncharacterized proteins (targets). Observations from several research groups concur that statistically significant matches can indicate functionally related active sites. One major subproblem is the design of effective motifs, which have many matches to functionally related targets (sensitive motifs), and few matches to functionally unrelated targets (specific motifs). Current techniques select and combine structural, physical, and evolutionary properties to generate motifs that mirror functional characteristics in active sites. This approach ignores incidental similarities that may occur with functionally unrelated proteins. To address this problem, we have developed Geometric Sieving (GS), a parallel distributed algorithm that efficiently refines motifs, designed by existing methods, into optimized motifs with maximal geometric and chemical dissimilarity from all known protein structures. In exhaustive comparison of all possible motifs based on the active sites of 10 well-studied proteins, we observed that optimized motifs were among the most sensitive and specific.     

Modeling protein-protein,protein-peptide,and protein-oligosaccharide complexes: CAPRI 7th edition

We present the seventh report on the performance of methods for predicting the atomic resolution structures of protein complexes offered as targets to the community-wide initiative on the Critical Assessment of Predicted Interactions. Performance was evaluated on the basis of 36 114 models of protein complexes submitted by 57 groups—including 13 automatic servers—in prediction rounds held during the years 2016 to 2019 for eight protein-protein, three protein-peptide, and five protein-oligosaccharide targets with different length ligands. Six of the protein-protein targets represented challenging hetero-complexes, due to factors such as availability of distantly related templates for the individual subunits, or for the full complex, inter-domain flexibility, conformational adjustments at the binding region, or the multi-component nature of the complex. The main challenge for the protein-peptide and protein-oligosaccharide complexes was to accurately model the ligand conformation and its interactions at the interface. Encouragingly, models of acceptable quality, or better, were obtained for a total of six protein-protein complexes, which included four of the challenging hetero-complexes and a homo-decamer. But fewer of these targets were predicted with medium or higher accuracy. High accuracy models were obtained for two of the three protein-peptide targets, and for one of the protein-oligosaccharide targets. The remaining protein-sugar targets were predicted with medium accuracy. Our analysis indicates that progress in predicting increasingly challenging and diverse types of targets is due to closer integration of template-based modeling techniques with docking, scoring, and model refinement procedures, and to significant incremental improvements in the underlying methodologies.     

Pilot studies on the parallel production of soluble mouse proteins in a bacterial expression system

We investigated the parallel production in medium throughput of mouse proteins, using protocols that involved recombinatorial cloning, protein expression screening and batch purification. The methods were scaled up to allow the simultaneous processing of tens or hundreds of protein samples. Scale-up was achieved in two stages. In an initial study, 30 targets were processed manually but with common protocols for all targets. In the second study, these protocols were applied to 96 target proteins that were processed in an automated manner. The success rates at each stage of the study were similar for both the manual and automated approaches. Overall, 15 of the selected 126 target mouse genes (12%) yielded soluble protein products in a bacterial expression system. This success rate compares favourably with other protein screening projects, particularly for eukaryotic proteins, and could be further improved by modifications at the cloning step.     

Structural genomics target selection for the New York consortium on membrane protein structure

The New York Consortium on Membrane Protein Structure (NYCOMPS), a part of the Protein Structure Initiative (PSI) in the USA, has as its mission to establish a high-throughput pipeline for determination of novel integral membrane protein structures. Here we describe our current target selection protocol, which applies structural genomics approaches informed by the collective experience of our team of investigators. We first extract all annotated proteins from our reagent genomes, i.e. the 96 fully sequenced prokaryotic genomes from which we clone DNA. We filter this initial pool of sequences and obtain a list of valid targets. NYCOMPS defines valid targets as those that, among other features, have at least two predicted transmembrane helices, no predicted long disordered regions and, except for community nominated targets, no significant sequence similarity in the predicted transmembrane region to any known protein structure. Proteins that feed our experimental pipeline are selected by defining a protein seed and searching the set of all valid targets for proteins that are likely to have a transmembrane region structurally similar to that of the seed. We require sequence similarity aligning at least half of the predicted transmembrane region of seed and target. Seeds are selected according to their feasibility and/or biological interest, and they include both centrally selected targets and community nominated targets. As of December 2008, over 6,000 targets have been selected and are currently being processed by the experimental pipeline. We discuss how our target list may impact structural coverage of the membrane protein space.     

Analysis of protein features and machine learning algorithms for prediction of druggable proteins

Background: Computational tools have been widely used in drug discovery process since they reduce the time and cost. Prediction of whether a protein is druggable is fundamental and crucial for drug research pipeline. Sequence based protein function prediction plays vital roles in many research areas. Training data, protein features selection and machine learning algorithms are three indispensable elements that drive the successfulness of the models. Methods: In this study, we tested the performance of different combinations of protein features and machine learning algorithms, based on FDA-approved small molecules’ targets, in druggable proteins prediction. We also enlarged the dataset to include the targets of small molecules that were in experiment or clinical investigation. Results: We found that although the 146-d vector used by Li et al. with neuron network achieved the best training accuracy of 91.10%, overlapped 3-gram word2vec with logistic regression achieved best prediction accuracy on independent test set (89.55%) and on newly approved-targets. Enlarged dataset with targets of small molecules in experiment and clinical investigation were trained. Unfortunately, the best training accuracy was only 75.48%. In addition, we applied our models to predict potential targets for references in future study. Conclusions: Our study indicates the potential ability of word2vec in the prediction of druggable protein. And the training dataset of druggable protein should not be extended to targets that are lack of verification. The target prediction package could be found on https://github.com/pkumdl/target_prediction.     

The New York Consortium on Membrane Protein Structure (NYCOMPS): a high-throughput platform for structural genomics of integral membrane proteins

The New York Consortium on Membrane Protein Structure (NYCOMPS) was formed to accelerate the acquisition of structural information on membrane proteins by applying a structural genomics approach. NYCOMPS comprises a bioinformatics group, a centralized facility operating a high-throughput cloning and screening pipeline, a set of associated wet labs that perform high-level protein production and structure determination by x-ray crystallography and NMR, and a set of investigators focused on methods development. In the first three years of operation, the NYCOMPS pipeline has so far produced and screened 7,250 expression constructs for 8,045 target proteins. Approximately 600 of these verified targets were scaled up to levels required for structural studies, so far yielding 24 membrane protein crystals. Here we describe the overall structure of NYCOMPS and provide details on the high-throughput pipeline.     

Rapid,robotic, small‐scale protein production for NMR screening and structure determination

Three‐dimensional protein structure determination is a costly process due in part to the low success rate within groups of potential targets. Conventional validation methods eliminate the vast majority of proteins from further consideration through a time‐consuming succession of screens for expression, solubility, purification, and folding. False negatives at each stage incur unwarranted reductions in the overall success rate. We developed a semi‐automated protocol for isotopically‐labeled protein production using the Maxwell‐16, a commercially available bench top robot, that allows for single‐step target screening by 2D NMR. In the span of a week, one person can express, purify, and screen 48 different ¹⁵N‐labeled proteins, accelerating the validation process by more than 10‐fold. The yield from a single channel of the Maxwell‐16 is sufficient for acquisition of a high‐quality 2D ¹H‐¹⁵N‐HSQC spectrum using a 3‐mm sample cell and 5‐mm cryogenic NMR probe. Maxwell‐16 screening of a control group of proteins reproduced previous validation results from conventional small‐scale expression screening and large‐scale production approaches currently employed by our structural genomics pipeline. Analysis of 18 new protein constructs identified two potential structure targets that included the second PDZ domain of human Par‐3. To further demonstrate the broad utility of this production strategy, we solved the PDZ2 NMR structure using [U‐¹⁵N,¹³C] protein prepared using the Maxwell‐16. This novel semi‐automated protein production protocol reduces the time and cost associated with NMR structure determination by eliminating unnecessary screening and scale‐up steps.     

Cystine-knot peptides: emerging tools for cancer imaging and therapy

Cystine-knot miniproteins, also known as knottins, constitute a large family of structurally related peptides with diverse amino acid sequences and biological functions. Knottins have emerged as attractive candidates for drug development as they potentially fill a niche between small molecules and protein biologics, offering drug-like properties and the ability to bind to clinical targets with high affinity and selectivity. Due to their extremely high stability and unique structural features, knottins also demonstrate promise in addressing challenging drug development goals, including the potential for oral delivery and the ability to access intracellular drug targets. Several naturally-occurring knottins have recently received approval for treating chronic pain and irritable bowel syndrome, while others are under development for tumor imaging applications. To expand beyond nature’s repertoire, rational and combinatorial protein engineering methods are generating tumor-targeting knottins for use as cancer diagnostics and therapeutics.     

The Center for Optimized Structural Studies (COSS) platform for automation in cloning,expression, and purification of single proteins and protein–protein complexes

Expression in Escherichia coli represents the simplest and most cost effective means for the production of recombinant proteins. This is a routine task in structural biology and biochemistry where milligrams of the target protein are required in high purity and monodispersity. To achieve these criteria, the user often needs to screen several constructs in different expression and purification conditions in parallel. We describe a pipeline, implemented in the Center for Optimized Structural Studies, that enables the systematic screening of expression and purification conditions for recombinant proteins and relies on a series of logical decisions. We first use bioinformatics tools to design a series of protein fragments, which we clone in parallel, and subsequently screen in small scale for optimal expression and purification conditions. Based on a scoring system that assesses soluble expression, we then select the top ranking targets for large-scale purification. In the establishment of our pipeline, emphasis was put on streamlining the processes such that it can be easily but not necessarily automatized. In a typical run of about 2 weeks, we are able to prepare and perform small-scale expression screens for 20–100 different constructs followed by large-scale purification of at least 4–6 proteins. The major advantage of our approach is its flexibility, which allows for easy adoption, either partially or entirely, by any average hypothesis driven laboratory in a manual or robot-assisted manner.