New Methods to Improve Protein Structure Modeling

The abundance of different protein variants generated by alternative splicing (AS) is not fully represented in the Protein Structure Database (PDB). Among the already limited set of proteins with experimentally determined structures deposited in PDB, only a small fraction have structures ofmore than one splicing variant. In the absence of experimental data, computational methods can be used to predict the structure of protein isoforms. In this chapter, the established approaches to computational protein structure prediction will be briefly described, and their use for studying differences that result from AS will be detailed. In particular, it will be noted when a reliable structural model can be obtained for a given protein sequence. Topics of finding the best template structures for modeling individual protein domains, predicting the structure of long, multidomain proteins, and assessing the quality of theoretical models, together with error correction, will also be addressed. The suggested protocol will be illustrated by the structure prediction of isoform C of phosphotyrosine phosphatase (LMPTP-C). 54.1 Theoretical Background In 1961, based on studies with ribonuclease A, Christian Anfinsen hypothesized that the structure of a protein in its native environment would be determined by the protein’s amino acid sequence, and that this native conformation is the one in which the Gibbs free energy of the system is lowest [1]. Since then, the prediction of a protein’s structure from its amino acid sequence has become the “holy grail” of computational biology. However, despite great efforts having been made, a universal algorithm with which to infer protein structure has not yet been developed. Nonetheless, several methods have been developed that can provide useful models, depending on a variety of conditions. There are two major approaches for protein structure prediction. The first approach, termed “comparative modeling” or “template-based modeling,” is based on the experimental observation that evolutionarily related proteins usually retain similar structures, despite an accumulation of substitutions at the level of amino acid sequence, and that the structure changes very slowly compared to the sequence [2]. Thus, an experimentally determined structure of one protein can be used as a template to model the structure of another related protein (a modeling target) by simulating the process of evolution at the sequence level.Modification of the template to “transmute” it into the target requires only limited computation. Therefore, comparative modeling does not require special computer resources, and can be easily carried out on a personal workstation using computer programs that are simple to install and use. Template-based modeling requires that, for a given target sequence, a structurally similar template is identified, and a correct target–template sequence alignment is Alternative pre-mRNA Splicing: Theory and Protocols, First Edition. Edited by Stefan Stamm, Chris Smith, and Reinhard Luhrmann. 2012 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2012 by Wiley-VCH Verlag GmbH & Co. KGaA. j 583

Template-based modeling (TBM) is a major component of the critical assessment of protein structure prediction (CASP). In CASP10, some 41,740 predicted models submitted by 150 predictor groups were assessed as TBM predictions. The accuracy of protein structure prediction was assessed by geometric comparison with experimental X-ray crystal and NMR structures using a composite score that included both global alignment metrics and distance-matrix-based metrics. These included GDT-HA and GDC-all global alignment scores, and the superimposition-independent LDDT distance-matrix-based score. In addition, a superimposition-independent RPF metric, similar to that described previously for comparing protein models against experimental NMR data, was used for comparing predicted protein structure models against experimental protein structures. To score well on all four of these metrics, models must feature accurate predictions of both backbone and side-chain conformations. Performance rankings were determined independently for server and the combined server plus human-curated predictor groups. Final rankings were made using paired head-to-head Student's t-test analysis of raw metric scores among the top 25 performing groups in each category.

The incessant expansion of protein sequence data presents a huge challenge in the identification of their structures which relies on laborious and time-consuming experimental approaches. Homology modeling of protein structure is highly accepted as an alternative to experimental approach, but its efficacy tends to decline when the sequence homology between the target and template sequences is low due to the limitation of their sequence alignment. Here, we demonstrated that employing a structure-based fingerprint of a homologous protein set as a constraint for the alignment of distant template and target sequences could improve the efficiency of homology modeling by using lipocalin proteins as a model system. Nine distantly related kernel lipocalins were structurally aligned, which was used to design a structure-based fingerprint consisting of commonly conserved hydrophobic and hydrophilic residues. The fingerprint was introduced as constraint in the alignment of template and target lipocalins, and the homology modeling of target lipocalin was performed based on the aligned sequences. Although the efficiency was marginal, the approach showed a consistent improvement compared to the homology modeling of lipocalin structures without the constraint for sequence alignment.

Databases are the computerized platform where information is stored and can be retrieved easily by public users. Biological databases are the repositories of biological data. These biological data libraries contain facts and figures related to various disciplines of research including genomics, proteomics, microarray technology, metabolomics and phylogenetics. By using biological databases, a broad collection of essential biological information can be exploited ranging from function, structure and localization of gene, clinical consequences of mutation to similarity index among biological sequences and structures. Nowadays, different kinds of biological databases are available on the web. The present write up focuses on biological databases and bioinformatics tools for protein structure analysis. This review also aims to elaborate the searching schemes, available in different structural databases. The wide variety of different levels and types of information content related to 3D protein structures are available on web-based databases. Regarding the biological functions and 3D structures of various proteins, these databases provide a huge range of useful links, schematic diagrams as well as strategies for detailed analysis of proteins and other macromolecules structures. 3D structural illustration of proteins stored in structural databases is determined and visualized by X-ray crystallography, electron microscopy and NMR spectroscopy. On regular basis, a large number of protein structures are submitted by structural biologists, updated and curated by subject experts. Most familiar biological databases that store 3D protein and other macromolecules structures include, PDB, 3D Genomics, CATH, & SCOP. These databases contain valuable information of overall protein structures, domains and motif structures, protein-protein complex systems and complex of protein with other biomolecules.

BackgroundAs genome sequencing is becoming routine in biomedical research, the total number of protein sequences is increasing exponentially, recently reaching over 108 million. However, only a tiny portion of these proteins (i.e. ~75,000 or < 0.07%) have solved tertiary structures determined by experimental techniques. The gap between protein sequence and structure continues to enlarge rapidly as the throughput of genome sequencing techniques is much higher than that of protein structure determination techniques. Computational software tools for predicting protein structure and structural features from protein sequences are crucial to make use of this vast repository of protein resources.ResultsTo meet the need, we have developed a comprehensive MULTICOM toolbox consisting of a set of protein structure and structural feature prediction tools. These tools include secondary structure prediction, solvent accessibility prediction, disorder region prediction, domain boundary prediction, contact map prediction, disulfide bond prediction, beta-sheet topology prediction, fold recognition, multiple template combination and alignment, template-based tertiary structure modeling, protein model quality assessment, and mutation stability prediction.ConclusionsThese tools have been rigorously tested by many users in the last several years and/or during the last three rounds of the Critical Assessment of Techniques for Protein Structure Prediction (CASP7-9) from 2006 to 2010, achieving state-of-the-art or near performance. In order to facilitate bioinformatics research and technological development in the field, we have made the MULTICOM toolbox freely available as web services and/or software packages for academic use and scientific research. It is available at http://sysbio.rnet.missouri.edu/multicom_toolbox/.

<sec><p indent="0mm">AlphaFold2 (AF2), the first deep learning-based protein structure prediction model to achieve near-experimental accuracy, has revolutionized the field of structural biology. With its ability to predict protein structures at unprecedented levels of accuracy, AF2 has become an essential tool in structural biology. In parallel, the deep learning methodologies derived from AF2 have made significant advancements in a range of downstream applications, such as efficient sampling of protein conformational ensembles, protein sequence design, and ligand generation. These applications are underpinned by an accurate description of the protein-free energy landscape, which is critical for modeling protein behavior in various biological contexts. </sec><sec> Despite these advancements, challenges remain, particularly in the area of multi-conformation sampling. Traditional methods such as molecular dynamics (MD) and Monte Carlo (MC) simulations, which are based on Boltzmann statistics, can provide detailed insights into conformational distributions. However, the application of these methods is hindered by their high computational cost and limited efficiency, especially for large or complex systems. In contrast, existing generative deep learning models can rapidly generate protein conformations, but often struggle to incorporate biophysical constraints, and are limited in the power of predicting the accurate energy minima, avoiding steric clashes, or understanding physics such as electrostatic interactions. This lack of control over the physical space makes it difficult to generate conformations that are both physically realistic and biologically relevant. </sec><sec> Furthermore, the robustness of the initial protein structure is crucial for downstream applications, including protein-protein interaction predictions, molecular docking, and virtual screening, as well as reliable and efficient MD simulations. However, conventional structure repair tools often fail to accurately model missing regions, such as loops or partially resolved regions, which may lead to unrealistic geometries or energetically unfavorable conformations. </sec><sec> To address these limitations, we present FoldCopilot, an innovative suite based on AF2. FoldCopilot uses both target sequences and multiple sequence alignments (MSAs) as input prompts for the Evoformer module, leveraging a homology-based sequence perturbation mechanism that generates diverse local conformations without the need for retraining. This method allows for the efficient generation of protein conformations and at the same time maintains high fidelity with known structural templates and evolutionary constraints. In addition, FoldCopilot integrates structural templates and initial reference frames into the iterative refinement process, enabling the directed evolution and control over generated protein structures. This iterative process ensures that the generated conformations not only adhere to biophysical principles, such as realistic energy states and steric compatibility but also meet specific functional requirements dictated by the target application. </sec><sec> Our experimental results demonstrate that FoldCopilot excels in key tasks such as protein structure repair and localized multi-conformation generation. The method significantly outperforms traditional structure repair techniques, yielding more accurate and biologically relevant models for regions of missing data. Additionally, FoldCopilot shows a high capability to generate diverse conformations, making it an ideal tool for protein engineering, protein-ligand interaction predictions, and other structure-based drug design applications. </sec><sec> In summary, FoldCopilot bridges the gap between high-fidelity conformational sampling and the ability to control conformational space, offering a powerful approach to facilitate protein-molecule interaction predictions, structural modeling, and molecular dynamics simulations. </sec>

Modeller: Generation and Refinement of Homology-Based Protein Structure Models

Summary form only given. The understanding of protein structures is vital to determine the function of a protein and its interaction with DNA, RNA and enzyme. The information about its conformation can provide essential information for drug design and protein engineering. While there are over a million known protein sequences, only a limited number of protein structures are experimentally determined. Hence, prediction of protein structures from protein sequences using computer programs is an important step to unveil proteins' three dimensional conformation and functions. As a result, prediction of protein structures has profound theoretical and practical influence over biological study. In this talk, we would show how to use machine learning methods with various advanced encoding schemes and classifiers improve the accuracy of protein structure prediction. The explanation of how a decision is made is also important for improving protein structure prediction. The reasonable interpretation is not only useful to guide the "wet experiments", but also the extracted rules are helpful to integrate computational intelligence with symbolic AI systems for advanced deduction. Some preliminary results using SVM and decision tree for rule extraction and prediction interpretation would also be presented.

Recognizing Protein Substructure Similarity Using Segmental Threading

Proteins are essential parts of our life and participate in virtually every process within a cell. The understanding of protein structures is vital to determine the function of a protein. Protein structure prediction (PSP) from amino acid sequence is one of the high focus problems in bioinformatics today. This is due to the fact that the biological function of the protein is determined by its three dimensional structure. Thus, protein structure prediction is a fundamental area of computational biology. Its importance is intensed by large amounts of sequence data coming from PDB (Protein Data Bank) and the fact that experimentally methods such as X-ray crystallography or Nuclear Magnetic Resonance (NMR)which are used to determining protein structures remains very expensive and time consuming. For minimizing the time, computational methods are used for protein folding and structure prediction problem. In this paper results of protein p53 are discussed.

These huge amounts of protein structure data make it difficult to create explanatory and predictive models that are consistent with huge volume of data. Difficulty increase when large variety of heterogeneous approaches gathers data from multiple perspectives. In order to facilitate computational processing data, it is especially critical to develop standardized structured data representation model formats for proteomics data. In this paper, we describe a protein ontology model for integrating protein databases and deduce a structured vocabulary for understanding process of protein synthesis completely. Proposed protein ontology model provides biologists and scientists with a description of sequence, structure and functions of protein and also provides interpretation of various factors on final protein structure conformation. The structured vocabulary for protein data, describing protein ontology is composed of various type definitions for protein entry details, sequence and structural information of proteins, structural domain family of protein, cellular function of protein, chemical bonds present in the protein, and external constraints deciding final protein conformation. The proposed ontology model will provide easier ways to predict and understand proteins.

Structural genomics provides an important approach for characterizing and understanding systems biology. As a step toward better integrating protein three-dimensional (3D) structural information in cancer systems biology, we have constructed a Human Cancer Pathway Protein Interaction Network (HCPIN) by analysis of several classical cancer-associated signaling pathways and their physical protein-protein interactions. Many well known cancer-associated proteins play central roles as "hubs" or "bottlenecks" in the HCPIN. At least half of HCPIN proteins are either directly associated with or interact with multiple signaling pathways. Although some 45% of residues in these proteins are in sequence segments that meet criteria sufficient for approximate homology modeling (Basic Local Alignment Search Tool (BLAST) E-value <10(-6)), only approximately 20% of residues in these proteins are structurally covered using high accuracy homology modeling criteria (i.e. BLAST E-value <10(-6) and at least 80% sequence identity) or by actual experimental structures. The HCPIN Website provides a comprehensive description of this biomedically important multipathway network together with experimental and homology models of HCPIN proteins useful for cancer biology research. To complement and enrich cancer systems biology, the Northeast Structural Genomics Consortium is targeting >1000 human proteins and protein domains from the HCPIN for sample production and 3D structure determination. The long range goal of this effort is to provide a comprehensive 3D structure-function database for human cancer-associated proteins and protein complexes in the context of their interaction networks. The network-based target selection (BioNet) approach described here is an example of a general strategy for targeting co-functioning proteins by structural genomics projects.

Homo-oligomerization of proteins is abundant in nature, and is often intimately related with the physiological functions of proteins, such as in metabolism, signal transduction or immunity. Information on the homo-oligomer structure is therefore important to obtain a molecular-level understanding of protein functions and their regulation. Currently available web servers predict protein homo-oligomer structures either by template-based modeling using homo-oligomer templates selected from the protein structure database or by ab initio docking of monomer structures resolved by experiment or predicted by computation. The GalaxyHomomer server, freely accessible at http://galaxy.seoklab.org/homomer, carries out template-based modeling, ab initio docking or both depending on the availability of proper oligomer templates. It also incorporates recently developed model refinement methods that can consistently improve model quality. Moreover, the server provides additional options that can be chosen by the user depending on the availability of information on the monomer structure, oligomeric state and locations of unreliable/flexible loops or termini. The performance of the server was better than or comparable to that of other available methods when tested on benchmark sets and in a recent CASP performed in a blind fashion.

Most computational protein structure prediction methods are designed for either template based or template-free (ab initio) structure prediction. The approaches that integrate the prediction capabilities of both template-based modeling and template-free modeling are needed to synergistically combine the two kinds of methods to improve protein structure prediction. In this work, we develop a new method to integrate several protein structure prediction methods including our template-based MULTICOM server, our ab initio contact-based protein structure prediction method CONFOLD, our multi-template-based model generation tool MTMG, and locally installed external Rosetta, I-TASSER and RaptorX protein structure prediction tools to improve protein structure prediction of a fullspectrum difficulty ranging from easy, to medium and to hard. Our method participated in the 11(th) community-wide Critical Assessment of Techniques for Protein Structure Prediction (CASP11) in 2014 as MULTICOM-NOVEL server. It was ranked among top 10 methods for protein tertiary structure prediction according to the official CASP11 assessment, which demonstrates that integrating complementary modeling methods is useful for advancing protein structure prediction.

Motivation: The interpretation of cancer-related single-nucleotide variants (SNVs) considering the protein features they affect, such as known functional sites, protein–protein interfaces, or relation with already annotated mutations, might complement the annotation of genetic variants in the analysis of NGS data. Current tools that annotate mutations fall short on several aspects, including the ability to use protein structure information or the interpretation of mutations in protein complexes.Results: We present the Structure–PPi system for the comprehensive analysis of coding SNVs based on 3D protein structures of protein complexes. The 3D repository used, Interactome3D, includes experimental and modeled structures for proteins and protein–protein complexes. Structure–PPi annotates SNVs with features extracted from UniProt, InterPro, APPRIS, dbNSFP and COSMIC databases. We illustrate the usefulness of Structure–PPi with the interpretation of 1 027 122 non-synonymous SNVs from COSMIC and the 1000G Project that provides a collection of ∼172 700 SNVs mapped onto the protein 3D structure of 8726 human proteins (43.2% of the 20 214 SwissProt-curated proteins in UniProtKB release 2014_06) and protein–protein interfaces with potential functional implications.Availability and implementation: Structure–PPi, along with a user manual and examples, isavailable at http://structureppi.bioinfo.cnio.es/Structure, the code for local installations at https://github.com/Rbbt-WorkflowsContact:tpons@cnio.esSupplementary Information:Supplementary data are available at Bioinformatics online.