The ConSurf-HSSP database: the mapping of evolutionary conservation among homologs onto PDB structures

SiteFerret: Beyond Simple Pocket Identification in Proteins

We present a novel method for the automatic detection of pockets on protein molecular surfaces. The algorithm is based on an ad hoc hierarchical clustering of virtual probe spheres obtained from the geometrical primitives used by the NanoShaper software to build the solvent-excluded molecular surface. The final ranking of putative pockets is based on the Isolation Forest method, an unsupervised learning approach originally developed for anomaly detection. A detailed importance analysis of pocket features provides insight into which geometrical (clustering) and chemical (amino acidic composition) properties characterize a good binding site. The method also provides a segmentation of pockets into smaller subpockets. We prove that subpockets are a convenient representation to pinpoint the binding site with great precision. SiteFerret is outstanding in its versatility, accurately predicting a wide range of binding sites, from those binding small molecules to those binding peptides, including difficult shallow sites.

Designed active-site library reveals thousands of functional GFP variants

Mutations in a protein active site can lead to dramatic and useful changes in protein activity. The active site, however, is sensitive to mutations due to a high density of molecular interactions, substantially reducing the likelihood of obtaining functional multipoint mutants. We introduce an atomistic and machine-learning-based approach, called high-throughput Functional Libraries (htFuncLib), that designs a sequence space in which mutations form low-energy combinations that mitigate the risk of incompatible interactions. We apply htFuncLib to the GFP chromophore-binding pocket, and, using fluorescence readout, recover >16,000 unique designs encoding as many as eight active-site mutations. Many designs exhibit substantial and useful diversity in functional thermostability (up to 96 °C), fluorescence lifetime, and quantum yield. By eliminating incompatible active-site mutations, htFuncLib generates a large diversity of functional sequences. We envision that htFuncLib will be used in one-shot optimization of activity in enzymes, binders, and other proteins.

Computational analysis of structural and functional evaluation of the deleterious missense variants in the human CTLA4 gene

CTLA-4 is an immune checkpoint receptor that negatively regulates the T-cell function expressed after T-cell activation to break the immune response. The current study predicted the genomic analysis to explore the functional variations of missense SNPs in the human CTLA4 gene using PolyPhen2, SIFT, PANTHER, PROVEAN, Fathmm, Mutation Assessor, PhD-SNP, SNPs&GO, SNAP2, and MutPred2. Phylogenetic conservation protein was predicted by ConSurf. Protein structural analysis was carried out by I-Mutant3, MUpro, iStable2, PremPS, and ERIS servers. Molecular dynamics trajectory analysis (RMSD, RMSF, Rg, SASA, H-bonds, and PCA) was performed to analyze the dynamic behavior of native and mutant CTLA-4 at the atomic level. Our in-silico analysis suggested that C58S, G118R, P137Q, P137R, P137L, P138T, and G146L variants were predicted to be the most deleterious missense variants and highly conserved residues. Moreover, the molecular dynamics analysis proposed a decrease in the protein stability and compactness with the P137R and P138T highlighting the impact of these variants on the function of the CTLA-4 protein.Communicated by Ramaswamy H. Sarma.

Structural and Functional Impact of Damaging Nonsynonymous Single Nucleotide Polymorphisms (nsSNPs) on Human VPS35 Protein Using Computational Approaches

Parkinson's disease is the second most common progressive neurodegenerative movement disorder. Mutations in retromer complex subunit and VPS35 represent the second most common cause of late-onset familial Parkinson's disease. The mutation in VPS35 can disrupt the normal protein functions resulting in Parkinson's disease. The aim of this study was the identification of deleterious missense Single Nucleotide Polymorphisms (nsSNPs) and their structural and functional impact on the VPS35 protein. In this study, several insilico tools were used to identify deleterious and disease-associated nsSNPs. 3D structure of VPS35 protein was constructed through MODELLER 9.2, normalized using FOLDX, and evaluated through RAMPAGE and ERRAT whereas, FOLDX was used for mutagenesis. 25 ligands were obtained from literature and docked using PyRx 0.8 software. Based on the binding affinity, five ligands i.e., PG4, MSE, GOL, EDO, and CAF were further analyzed. Molecular Dynamic simulation analysis was performed using GROMACS 5.1.4, where temperature, pressure, density, RMSD, RMSF, Rg, and SASA graphs were analyzed. The results showed that the mutations Y67H, R524W, and D620N had a structural and functional impact on the VPS35 protein. The current findings will help in appropriate drug design against the disease caused by these mutations in a large population using in-vitro study.

In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets

Target identification is an important step in drug discovery, and computer-aided drug target identification methods are attracting more attention compared with traditional drug target identification methods, which are time-consuming and costly. Computer-aided drug target identification methods can greatly reduce the searching scope of experimental targets and associated costs by identifying the diseases-related targets and their binding sites and evaluating the druggability of the predicted active sites for clinical trials. In this review, we introduce the principles of computer-based active site identification methods, including the identification of binding sites and assessment of druggability. We provide some guidelines for selecting methods for the identification of binding sites and assessment of druggability. In addition, we list the databases and tools commonly used with these methods, present examples of individual and combined applications, and compare the methods and tools. Finally, we discuss the challenges and limitations of binding site identification and druggability assessment at the current stage and provide some recommendations and future perspectives.

A patent-based consideration of latest platforms in the art of directed evolution: a decade long untold story

Directed (or in vitro) evolution of proteins and metabolic pathways requires tools for creating genetic diversity and identifying protein variants with new or improved functional properties. Besides simplicity, reliability, speed, versatility, universal applicability and economy of the technique, the new science of synthetic biology requires improved means for construction of smart and high-quality mutant libraries to better navigate the sequence diversity. In vitro CRISPR/Cas9-mediated mutagenic (ICM) system and machine-learning (ML)-assisted approaches to directed evolution are now in the field to achieve the goal. This review describes the gene diversification strategies, screening and selection methods, in silico (computer-aided), Cas9-mediated and ML-based approaches to mutagenesis, developed especially in the last decade, and their patent position. The objective behind is to emphasize researchers the need for noting which mutagenesis, screening or selection method is patented and then selecting a suitable restriction-free approach to sequence diversity. Techniques and evolved products subject to patent rights need commercial license if their use is for purposes other than private or experimental research.

Identification and Mutation Analysis of Nonconserved Residues on the TIM-Barrel Surface of GH5_5 Cellulases for Catalytic Efficiency and Stability Improvement

Exploring the potential functions of nonconserved residues on the outer side of α-helices and systematically optimizing them are pivotal for their application in protein engineering. Based on the evolutionary structural conservation analysis of GH5_5 cellulases, a practical molecular improvement strategy was developed. Highly variable sites on the outer side of the α-helices of the GH5_5 cellulase from Aspergillus niger (AnCel5A) were screened, and 14 out of the 34 highly variable sites were confirmed to exert a positive effect on the activity. After the modular combination of the positive mutations, the catalytic efficiency of the mutants was further improved. By using CMC-Na as the substrate, the catalytic efficiency and specific activity of variant AnCel5A_N193A/T300P/D307P were approximately 2.0-fold that of AnCel5A (227 ± 21 versus 451 ± 43 ml/s/mg and 1,726 ± 19 versus 3,472 ± 42 U/mg, respectively). The half-life (t_1/2) of variant AnCel5A_N193A/T300P/D307P at 75°C was 2.36 times that of AnCel5A. The role of these sites was successfully validated in other GH5_5 cellulases. Computational analyses revealed that the flexibility of the loop 6-loop 7-loop 8 region was responsible for the increased catalytic performance. This work not only illustrated the important role of rapidly evolving positions on the outer side of the α-helices of GH5_5 cellulases but also revealed new insights into engineering the proteins that nature left as clues for us to find. IMPORTANCE A comprehensive understanding of the residues on the α-helices of the GH5_5 cellulases is important for catalytic efficiency and stability improvement. The main objective of this study was to use the evolutionary conservation and plasticity of the TIM-barrel fold to probe the relationship between nonconserved residues on the outer side of the α-helices and the catalytic efficiency of GH5_5 cellulases by conducting structure-guided protein engineering. By using a four-step nonconserved residue screening strategy, the functional role of nonconserved residues on the outer side of the α-helices was effectively identified, and a variant with superior performance and capability was constructed. Hence, this study proved the effectiveness of this strategy in engineering GH5_5 cellulases and provided a potential competitor for industrial applications. Furthermore, this study sheds new light on engineering TIM-barrel proteins.

Current status of PTMs structural databases: applications, limitations and prospects

Protein 3D structures, determined by their amino acid sequences, are the support of major crucial biological functions. Post-translational modifications (PTMs) play an essential role in regulating these functions by altering the physicochemical properties of proteins. By virtue of their importance, several PTM databases have been developed and released in decades, but very few of these databases incorporate real 3D structural data. Since PTMs influence the function of the protein and their aberrant states are frequently implicated in human diseases, providing structural insights to understand the influence and dynamics of PTMs is crucial for unraveling the underlying processes. This review is dedicated to the current status of databases providing 3D structural data on PTM sites in proteins. Some of these databases are general, covering multiple types of PTMs in different organisms, while others are specific to one particular type of PTM, class of proteins or organism. The importance of these databases is illustrated with two major types of in silico applications: predicting PTM sites in proteins using machine learning approaches and investigating protein structure-function relationships involving PTMs. Finally, these databases suffer from multiple problems and care must be taken when analyzing the PTMs data.

Molecular characterization and expression analysis of ribosomal L18/L5e gene in Pennisetum glaucum (L.) R. Br

Ribosomal L18/L5e (RL18/L5e) is a member of the ribosomal L18/L5e protein family, which has an essential function in translation of mRNA into protein in the large ribosomal subunit. In this study, RL18/L5e was isolated and sequenced from local Pennisetum glaucum (L.) R. Br. cultivar which is known to adapt to environmental stress. The obtained cDNA for PgRL18/L5e was 699 bp in length, with an open reading frame of 564 bp. The deduced protein sequence contained 187 amino acids and comprised an RL18/L5e domain, which shared high sequence identity with orthologous proteins from Viridiplantae. The obtained PgRL18/L5e cDNA contained two exons of 154 and 545 bp, respectively, and an intron of 1398 bp. Secondary and 3D structures of the deduced PgRL18/L5e protein were predicted using in silico tools. Phylogenetic analysis showed close relationships between the PgRL18/L5e protein and its orthologs from monocot species. Multiple sequence alignment showed high identity in the RL18/L5e domain sequence in all orthologous proteins in Viridiplantae. Moreover, all orthologous RL18/L5e proteins shared the same domain architecture and were nearly equal in length. Quantitative real-time PCR indicated a higher transcript abundance of PgRL18/L5e in shoots than in roots of 3-day-old seedlings. Moreover, the expression of PgRL18/L5e in seedlings under cold and drought stress was substantially lower than that in untreated seedlings, whereas the highest expression was shown under heat stress. This study provides insights into the structure and function of the RL18/L5e gene in tolerant crops, which could facilitate the understanding of the role of the various plant ribosomal proteins in adaptation to extreme environments.

Molecular Simulations and Network Modeling Reveal an Allosteric Signaling in the SARS-CoV-2 Spike Proteins

The development of computational strategies for the quantitative characterization of the functional mechanisms of SARS-CoV-2 spike proteins is of paramount importance in efforts to accelerate the discovery of novel therapeutic agents and vaccines combating the COVID-19 pandemic. Structural and biophysical studies have recently characterized the conformational landscapes of the SARS-CoV-2 spike glycoproteins in the prefusion form, revealing a spectrum of stable and more dynamic states. By employing molecular simulations and network modeling approaches, this study systematically examined functional dynamics and identified the regulatory centers of allosteric interactions for distinct functional states of the wild-type and mutant variants of the SARS-CoV-2 prefusion spike trimer. This study presents evidence that the SARS-CoV-2 spike protein can function as an allosteric regulatory engine that fluctuates between dynamically distinct functional states. Perturbation-based modeling of the interaction networks revealed a key role of the cross-talk between the effector hotspots in the receptor binding domain and the fusion peptide proximal region of the SARS-CoV-2 spike protein. The results have shown that the allosteric hotspots of the interaction networks in the SARS-CoV-2 spike protein can control the dynamic switching between functional conformational states that are associated with virus entry to the host receptor. This study offers a useful and novel perspective on the underlying mechanisms of the SARS-CoV-2 spike protein through the lens of allosteric signaling as a regulatory apparatus of virus transmission that could open up opportunities for targeted allosteric drug discovery against SARS-CoV-2 proteins and contribute to the rapid response to the current and potential future pandemic scenarios.

Predicting binding sites from unbound versus bound protein structures

We present the application of seven binding-site prediction algorithms to a meticulously curated dataset of ligand-bound and ligand-free crystal structures for 304 unique protein sequences (2528 crystal structures). We probe the influence of starting protein structures on the results of binding-site prediction, so the dataset contains a minimum of two ligand-bound and two ligand-free structures for each protein. We use this dataset in a brief survey of five geometry-based, one energy-based, and one machine-learning-based methods: Surfnet, Ghecom, LIGSITE_csc, Fpocket, Depth, AutoSite, and Kalasanty. Distributions of the F scores and Matthew’s correlation coefficients for ligand-bound versus ligand-free structure performance show no statistically significant difference in structure type versus performance for most methods. Only Fpocket showed a statistically significant but low magnitude enhancement in performance for holo structures. Lastly, we found that most methods will succeed on some crystal structures and fail on others within the same protein family, despite all structures being relatively high-quality structures with low structural variation. We expected better consistency across varying protein conformations of the same sequence. Interestingly, the success or failure of a given structure cannot be predicted by quality metrics such as resolution, Cruickshank Diffraction Precision index, or unresolved residues. Cryptic sites were also examined.

Exploring Mechanisms of Communication Switching in the Hsp90-Cdc37 Regulatory Complexes with Client Kinases through Allosteric Coupling of Phosphorylation Sites: Perturbation-Based Modeling and Hierarchical Community Analysis of Residue Interaction Networks

Understanding molecular principles underlying chaperone-based modulation of kinase client activity is critically important to dissect functions and activation mechanisms of many oncogenic proteins. The recent experimental studies have suggested that phosphorylation sites in the Hsp90 and Cdc37 proteins can serve as conformational communication switches of chaperone regulation and kinase interactions. However, a mechanism of allosteric coupling between phosphorylation sites in the Hsp90 and Cdc37 during client binding is poorly understood, and the molecular signatures underpinning specific roles of phosphorylation sites in the Hsp90 regulation remain unknown. In this work, we employed a combination of evolutionary analysis, coarse-grained molecular simulations together with perturbation-based network modeling and scanning of the unbound and bound Hsp90 and Cdc37 structures to quantify allosteric effects of phosphorylation sites and identify unique signatures that are characteristic for communication switches of kinase-specific client binding. By using network-based metrics of the dynamic intercommunity bridgeness and community centrality, we characterize specific signatures of phosphorylation switches involved in allosteric regulation. Through perturbation-based analysis of the dynamic residue interaction networks, we show that mutations of kinase-specific phosphorylation switches can induce long-range effects and lead to a global rewiring of the allosteric network and signal transmission in the Hsp90-Cdc37-kinase complex. We determine a specific group of phosphorylation sites in the Hsp90 where mutations may have a strong detrimental effect on allosteric interaction network, providing insight into the mechanism of phosphorylation-induced communication switching. The results demonstrate that kinase-specific phosphorylation switches of communications in the Hsp90 may be partly predisposed for their regulatory role based on preexisting allosteric propensities.

BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning approaches

As the first web server to analyze various biological sequences at sequence level based on machine learning approaches, many powerful predictors in the field of computational biology have been developed with the assistance of the BioSeq-Analysis. However, the BioSeq-Analysis can be only applied to the sequence-level analysis tasks, preventing its applications to the residue-level analysis tasks, and an intelligent tool that is able to automatically generate various predictors for biological sequence analysis at both residue level and sequence level is highly desired. In this regard, we decided to publish an important updated server covering a total of 26 features at the residue level and 90 features at the sequence level called BioSeq-Analysis2.0 (http://bliulab.net/BioSeq-Analysis2.0/), by which the users only need to upload the benchmark dataset, and the BioSeq-Analysis2.0 can generate the predictors for both residue-level analysis and sequence-level analysis tasks. Furthermore, the corresponding stand-alone tool was also provided, which can be downloaded from http://bliulab.net/BioSeq-Analysis2.0/download/. To the best of our knowledge, the BioSeq-Analysis2.0 is the first tool for generating predictors for biological sequence analysis tasks at residue level. Specifically, the experimental results indicated that the predictors developed by BioSeq-Analysis2.0 can achieve comparable or even better performance than the existing state-of-the-art predictors.

BioSeq-Analysis: a platform for DNA, RNA and protein sequence analysis based on machine learning approaches

With the avalanche of biological sequences generated in the post-genomic age, one of the most challenging problems is how to computationally analyze their structures and functions. Machine learning techniques are playing key roles in this field. Typically, predictors based on machine learning techniques contain three main steps: feature extraction, predictor construction and performance evaluation. Although several Web servers and stand-alone tools have been developed to facilitate the biological sequence analysis, they only focus on individual step. In this regard, in this study a powerful Web server called BioSeq-Analysis (http://bioinformatics.hitsz.edu.cn/BioSeq-Analysis/) has been proposed to automatically complete the three main steps for constructing a predictor. The user only needs to upload the benchmark data set. BioSeq-Analysis can generate the optimized predictor based on the benchmark data set, and the performance measures can be reported as well. Furthermore, to maximize user's convenience, its stand-alone program was also released, which can be downloaded from http://bioinformatics.hitsz.edu.cn/BioSeq-Analysis/download/, and can be directly run on Windows, Linux and UNIX. Applied to three sequence analysis tasks, experimental results showed that the predictors generated by BioSeq-Analysis even outperformed some state-of-the-art methods. It is anticipated that BioSeq-Analysis will become a useful tool for biological sequence analysis.

Anticodon-binding domain swapping in a nondiscriminating aspartyl-tRNA synthetase reveals contributions to tRNA specificity and catalytic activity

The nondiscriminating aspartyl-tRNA synthetase (ND-AspRS), found in many archaea and bacteria, covalently attaches aspartic acid to tRNA^Asp and tRNA^Asn generating a correctly charged Asp-tRNA^Asp and an erroneous Asp-tRNA^Asn . This relaxed tRNA specificity is governed by interactions between the tRNA and the enzyme. In an effort to assess the contributions of the anticodon-binding domain to tRNA specificity, we constructed two chimeric enzymes, Chimera-D and Chimera-N, by replacing the native anticodon-binding domain in the Helicobacter pylori ND-AspRS with that of a discriminating AspRS (Chimera-D) and an asparaginyl-tRNA synthetase (AsnRS, Chimera-N), both from Escherichia coli. Both chimeric enzymes showed similar secondary structure compared to wild-type (WT) ND-AspRS and maintained the ability to form dimeric complexes in solution. Although less catalytically active than WT, Chimera-D was more discriminating as it aspartylated tRNA^Asp over tRNA^Asn with a specificity ratio of 7.0 compared to 2.9 for the WT enzyme. In contrast, Chimera-N exhibited low catalytic activity toward tRNA^Asp and was unable to aspartylate tRNA^Asn . The observed catalytic activities for the two chimeras correlate with their heterologous toxicity when expressed in E. coli. Molecular dynamics simulations show a reduced hydrogen bond network at the interface between the anticodon-binding domain and the catalytic domain in Chimera-N compared to Chimera-D or WT, explaining its lower stability and catalytic activity.

Overexpression of Osmosensitive Ca2+-Permeable Channel TMEM63B Promotes Migration in HEK293T Cells

The recent discovery of the osmosensitive calcium (Ca²⁺) channel OSCA has revealed the potential mechanism by which plant cells sense diverse stimuli. Osmosensory transporters and mechanosensitive channels can detect and respond to osmotic shifts that play an important role in active cell homeostasis. Members of the TMEM63 family of proteins are described as the closest homologues of OSCAs. Here, we characterize TMEM63B, a mammalian homologue of OSCAs, recently classified as mechanosensitive. In HEK293T cells, TMEM63B localizes to the plasma membrane and is associated with F-actin. This Ca²⁺-permeable channel specifically induces Ca²⁺ influx across the membrane in response to extracellular Ca²⁺ concentration and hyperosmolarity. In addition, overexpression of TMEM63B in HEK293T cells significantly enhanced cell migration and wound healing. The link between Ca²⁺ osmosensitivity and cell migration might help to establish TMEM63B's pathogenesis, for example, in cancer in which it is frequently overexpressed.

NAA10 dysfunction with normal NatA-complex activity in a girl with non-syndromic ID and a de novo NAA10 p.(V111G) variant - a case report

Background

The NAA10-NAA15 (NatA) protein complex is an N-terminal acetyltransferase responsible for acetylating ~ 40% of eukaryotic proteins. In recent years, NAA10 variants have been found in patients with an X-linked developmental disorder called Ogden syndrome in its most severe form and, in other familial or de novo cases, with variable degrees of syndromic intellectual disability (ID) affecting both sexes.

Case presentation

Here we report and functionally characterize a novel and de novo NAA10 (NM_003491.3) c.332 T > G p.(V111G) missense variant, that was detected by trio-based whole exome sequencing in an 11 year old girl with mild/moderate non-syndromic intellectual disability. She had delayed motor and language development, but normal behavior without autistic traits. Her blood leukocyte X-inactivation pattern was within normal range (80/20). Functional characterization of NAA10-V111G by cycloheximide chase experiments suggests that NAA10-V111G has a reduced stability compared to NAA10-WT, and in vitro acetylation assays revealed a reduced enzymatic activity of monomeric NAA10-V111G but not for NAA10-V111G in complex with NAA15 (NatA enzymatic activity).

Conclusions

We show that NAA10-V111G has a reduced stability and monomeric catalytic activity, while NatA function remains unaltered. This is the first example of isolated NAA10 dysfunction in a case of ID, suggesting that the syndromic cases may also require a degree of compromised NatA function.

Validation of a multi-omics strategy for prioritizing personalized candidate driver genes

Significant heterogeneity between different tumors prevents the discovery of cancer driver genes, especially in a patient-specific manner. We previously prioritized five personalized candidate mutation-driver genes in a hyper-mutated hepatocellular carcinoma patient using a multi-omics strategy. However, the roles of the prioritized driver genes and patient-specific mutations in hepatocarcinogenesis are unclear. We investigated the impact of the tumor-mutated allele on structure-function relationship of the encoded protein and assessed both loss- and gain-of-function of these genes and mutations on hepatoma cell behaviors in vitro. The prioritized mutation-driver genes act as tumor suppressor genes and inhibit cell proliferation and migration. In addition, the loss-of-function effect of the patient-specific mutations promoted cell proliferation and migration. Of note, the HNF1A S247T mutation significantly reduced the HNF1A transcriptional activity for hepatocyte nuclear factor 4 alpha (HNF4A) but did not disrupt nuclear localization of HNF1A. The results provide evidence for supporting the validity of our proposed multi-omics strategy, which supplies a new avenue for prioritizing mutation-drivers towards personalized cancer therapy.

Beyond the Natural Proteome: Nondegenerate Saturation Mutagenesis-Methodologies and Advantages

Beyond the natural proteome, high-throughput mutagenesis offers the protein engineer an opportunity to "tweak" the wild-type activity of a protein to create a recombinant protein with required attributes. Of the various approaches available, saturation mutagenesis is one of the core techniques employed by protein engineers, and in recent times, nondegenerate saturation mutagenesis is emerging as the approach of choice. This review compares the current methodologies available for conducting nondegenerate saturation mutagenesis with traditional, degenerate saturation and briefly outlines the options available for screening the resulting libraries, to discover a novel protein with the required activity and/or specificity.

Improving the performance of the PLB index for ligand-binding site prediction using dihedral angles and the solvent-accessible surface area

Protein ligand-binding site prediction is highly important for protein function determination and structure-based drug design. Over the past twenty years, dozens of computational methods have been developed to address this problem. Soga et al. identified ligand cavities based on the preferences of amino acids for the ligand-binding site (RA) and proposed the propensity for ligand binding (PLB) index to rank the cavities on the protein surface. However, we found that residues exhibit different RAs in response to changes in solvent exposure. Furthermore, previous studies have suggested that some dihedral angles of amino acids in specific regions of the Ramachandran plot are preferred at the functional sites of proteins. Based on these discoveries, the amino acid solvent-accessible surface area and dihedral angles were combined with the RA and PLB to obtain two new indexes, multi-factor RA (MF-RA) and multi-factor PLB (MF-PLB). MF-PLB, PLB and other methods were tested using two benchmark databases and two particular ligand-binding sites. The results show that MF-PLB can improve the success rate of PLB for both ligand-bound and ligand-unbound structures, particularly for top choice prediction.

Impact of Binding Site Comparisons on Medicinal Chemistry and Rational Molecular Design

Modern rational drug design not only deals with the search for ligands binding to interesting and promising validated targets but also aims to identify the function and ligands of yet uncharacterized proteins having impact on different diseases. Additionally, it contributes to the design of inhibitors with distinct selectivity patterns and the prediction of possible off-target effects. The identification of similarities between binding sites of various proteins is a useful approach to cope with those challenges. The main scope of this perspective is to describe applications of different protein binding site comparison approaches to outline their applicability and impact on molecular design. The article deals with various substantial application domains and provides some outstanding examples to show how various binding site comparison methods can be applied to promote in silico drug design workflows. In addition, we will also briefly introduce the fundamental principles of different protein binding site comparison methods.

Prioritizing functional phosphorylation sites based on multiple feature integration

Protein phosphorylation is an important type of post-translational modification that is involved in a variety of biological activities. Most phosphorylation events occur on serine, threonine and tyrosine residues in eukaryotes. In recent years, many phosphorylation sites have been identified as a result of advances in mass-spectrometric techniques. However, a large percentage of phosphorylation sites may be non-functional. Systematically prioritizing functional sites from a large number of phosphorylation sites will be increasingly important for the study of their biological roles. This study focused on exploring the intrinsic features of functional phosphorylation sites to predict whether a phosphosite is likely to be functional. We found significant differences in the distribution of evolutionary conservation, kinase association, disorder score, and secondary structure between known functional and background phosphorylation datasets. We built four different types of classifiers based on the most representative features and found that their performances were similar. We also prioritized 213,837 human phosphorylation sites from a variety of phosphorylation databases, which will be helpful for subsequent functional studies. All predicted results are available for query and download on our website (Predict Functional Phosphosites, PFP, http://pfp.biosino.org/).

Understanding the fabric of protein crystals: computational classification of biological interfaces and crystal contacts

Modern structural biology still draws the vast majority of information from crystallography, a technique where the objects being investigated are embedded in a crystal lattice. Given the complexity and variety of those objects, it becomes fundamental to computationally assess which of the interfaces in the lattice are biologically relevant and which are simply crystal contacts. Since the mid-1990s, several approaches have been applied to obtain high-accuracy classification of crystal contacts and biological protein–protein interfaces. This review provides an overview of the concepts and main approaches to protein interface classification: thermodynamic estimation of interface stability, evolutionary approaches based on conservation of interface residues, and co-occurrence of the interface across different crystal forms. Among the three categories, evolutionary approaches offer the strongest promise for improvement, thanks to the incessant growth in sequence knowledge. Importantly, protein interface classification algorithms can also be used on multimeric structures obtained using other high-resolution techniques or for protein assembly design or validation purposes. A key issue linked to protein interface classification is the identification of the biological assembly of a crystal structure and the analysis of its symmetry. Here, we highlight the most important concepts and problems to be overcome in assembly prediction. Over the next few years, tools and concepts of interface classification will probably become more frequently used and integrated in several areas of structural biology and structural bioinformatics. Among the main challenges for the future are better addressing of weak interfaces and the application of interface classification concepts to prediction problems like protein–protein docking.

Supplementary information: Supplementary data are available at Bioinformatics online.

Contact:guido.capitani@psi.ch

On the Origin of SERKs: Bioinformatics Analysis of the Somatic Embryogenesis Receptor Kinases

Somatic embryogenesis receptor-like kinases (SERKs) are leucine-rich repeat receptor-like kinases involved in several, seemingly unrelated, plant-signaling pathways. In Arabidopsis thaliana, functional and genetic analysis of four SERK proteins has indicated that they are only partly redundant; their functions overlap but each performs a specific subset of signaling roles. The molecular basis for the functional specificity within this highly homologous protein family is currently not known. Sequence analysis of SERK proteins from different plant species indicates that the SERKs are a highly conserved protein family present in monocots, dicots, and non-vascular plants. Residues in the extracellular domain that are important for interaction with other receptor kinases are highly conserved, even among SERK members without a function in the corresponding pathways. SERK2, for instance, does not function in the brassinosteroid pathway, does not interact with BRI1, but is conserved in its BRI1-interacting domain. Further sequence analysis indicates that SERK3/BAK1 and SERK4/BKK1 have diverged from the original SERK protein in both their extracellular and cytoplasmic domains. Functional analysis of chimeric SERK proteins shows that different domains provide the SERK proteins with different functional specificity. For instance, the SERK1 or SERK2 extracellular domains are essential for SERK function in male sporogenesis, while the SERK3 extracellular and cytoplasmic domains are essential for SERK3 activity in brassinosteroid and flagellin signaling. The emerging picture is that SERKs are ancient genes, whose products have been recruited as co-receptors in the newly evolved signaling pathways. The SERK ligand-binding and protein-protein interaction domains are highly conserved, allowing all SERKs to form complexes, albeit with different affinity. However, specific functional residues must have been altered, in both the extracellular and intracellular domains, to allow for the observed differences in functionality.

High incidence of unrecognized visceral/neurological late-onset Niemann-Pick disease, type C1, predicted by analysis of massively parallel sequencing data sets

Purpose

Niemann-Pick disease, type C (NPC) is a recessive, neurodegenerative, lysosomal storage disease caused by mutations in either NPC1 or NPC2. The diagnosis is difficult and frequently delayed. Ascertainment is likely incomplete due to both these factors and that the full phenotypic spectrum may not have been fully delineated. Given the recent development of a blood-based diagnostic test and development of potential therapies, it is important to understand the incidence of NPC and to define at risk patient populations.

Method

We evaluated data from four large massively parallel exome sequencing data sets. Variant sequences were identified and classified as pathogenic or non-pathogenic based on a combination of literature review and bioinformatic analysis. This methodology provided an unbiased approach to determining the allele frequency.

Results

Our data suggests an incidence rate for NPC1 and NPC2 of 1/92,104 and 1/2,858,998, respectively. However, evaluation of common NPC1 variants, suggests that there may be a late-onset NPC1phenotype with a markedly higher incidence on the order of 1/20,000-39,000.

Conclusions

We determined a combined incidence of classical NPC of 1/89,229 or 1.12 affected patients per 100,000 conceptions, but predict incomplete ascertainment of a late-onset phenotype of NPC1. This finding strongly supports the need for increased screening of potential patients.

Scop3D: three-dimensional visualization of sequence conservation

The integration of a protein's structure with its known sequence variation provides insight on how that protein evolves, for instance in terms of (changing) function or immunogenicity. Yet, collating the corresponding sequence variants into a multiple sequence alignment, calculating each position's conservation, and mapping this information back onto a relevant structure is not straightforward. We therefore built the Sequence Conservation on Protein 3D structure (scop3D) tool to perform these tasks automatically. The output consists of two modified PDB files in which the B-values for each position are replaced by the percentage sequence conservation, or the information entropy for each position, respectively. Furthermore, text files with absolute and relative amino acid occurrences for each position are also provided, along with snapshots of the protein from six distinct directions in space. The visualization provided by scop3D can for instance be used as an aid in vaccine development or to identify antigenic hotspots, which we here demonstrate based on an analysis of the fusion proteins of human respiratory syncytial virus and mumps virus.

Algorithmic approaches to protein-protein interaction site prediction

Interaction sites on protein surfaces mediate virtually all biological activities, and their identification holds promise for disease treatment and drug design. Novel algorithmic approaches for the prediction of these sites have been produced at a rapid rate, and the field has seen significant advancement over the past decade. However, the most current methods have not yet been reviewed in a systematic and comprehensive fashion. Herein, we describe the intricacies of the biological theory, datasets, and features required for modern protein-protein interaction site (PPIS) prediction, and present an integrative analysis of the state-of-the-art algorithms and their performance. First, the major sources of data used by predictors are reviewed, including training sets, evaluation sets, and methods for their procurement. Then, the features employed and their importance in the biological characterization of PPISs are explored. This is followed by a discussion of the methodologies adopted in contemporary prediction programs, as well as their relative performance on the datasets most recently used for evaluation. In addition, the potential utility that PPIS identification holds for rational drug design, hotspot prediction, and computational molecular docking is described. Finally, an analysis of the most promising areas for future development of the field is presented.

Exploring the enantioselective mechanism of halohydrin dehalogenase from Agrobacterium radiobacter AD1 by iterative saturation mutagenesis

Halohydrin dehalogenase from Agrobacterium radiobacter AD1 (HheC) shows great potential in producing valuable chiral epoxides and β-substituted alcohols. The wild-type (WT) enzyme displays a high R-enantiopreference toward most aromatic substrates, whereas no S-selective HheC has been reported to date. To obtain more enantioselective enzymes, seven noncatalytic active-site residues were subjected to iterative saturation mutagenesis (ISM). After two rounds of screening aspects of both activity and enantioselectivity (E), three outstanding mutants (Thr134Val/Leu142Met, Leu142Phe/Asn176His, and Pro84Val/Phe86Pro/Thr134Ala/Asn176Ala mutants) with divergent enantioselectivity were obtained. The two double mutants displayed approximately 2-fold improvement in R-enantioselectivity toward 2-chloro-1-phenylethanol (2-CPE) without a significant loss of enzyme activity compared with the WT enzyme. Strikingly, the Pro84Val/Phe86Pro/Thr134Ala/Asn176Ala mutant showed an inverted enantioselectivity (from an ER of 65 [WT] to an ES of 101) and approximately 100-fold-enhanced catalytic efficiency toward (S)-2-CPE. Molecular dynamic simulation and docking analysis revealed that the phenyl side chain of (S)-2-CPE bound at a different location than that of its R-counterpart; those mutations generated extra connections for the binding of the favored enantiomer, while the eliminated connections reduced binding of the nonfavored enantiomer, all of which could contribute to the observed inverted enantiopreference.

Developing eThread pipeline using SAGA-pilot abstraction for large-scale structural bioinformatics

While most of computational annotation approaches are sequence-based, threading methods are becoming increasingly attractive because of predicted structural information that could uncover the underlying function. However, threading tools are generally compute-intensive and the number of protein sequences from even small genomes such as prokaryotes is large typically containing many thousands, prohibiting their application as a genome-wide structural systems biology tool. To leverage its utility, we have developed a pipeline for eThread—a meta-threading protein structure modeling tool, that can use computational resources efficiently and effectively. We employ a pilot-based approach that supports seamless data and task-level parallelism and manages large variation in workload and computational requirements. Our scalable pipeline is deployed on Amazon EC2 and can efficiently select resources based upon task requirements. We present runtime analysis to characterize computational complexity of eThread and EC2 infrastructure. Based on results, we suggest a pathway to an optimized solution with respect to metrics such as time-to-solution or cost-to-solution. Our eThread pipeline can scale to support a large number of sequences and is expected to be a viable solution for genome-scale structural bioinformatics and structure-based annotation, particularly, amenable for small genomes such as prokaryotes. The developed pipeline is easily extensible to other types of distributed cyberinfrastructure.

MDC-Analyzer: a novel degenerate primer design tool for the construction of intelligent mutagenesis libraries with contiguous sites

Recent computational and bioinformatics advances have enabled the efficient creation of novel biocatalysts by reducing amino acid variability at hot spot regions. To further expand the utility of this strategy, we present here a tool called Multi-site Degenerate Codon Analyzer (MDC-Analyzer) for the automated design of intelligent mutagenesis libraries that can completely cover user-defined randomized sequences, especially when multiple contiguous and/or adjacent sites are targeted. By initially defining an objective function, the possible optimal degenerate PCR primer profiles could be automatically explored using the heuristic approach of Greedy Best-First-Search. Compared to the previously developed DC-Analyzer, MDC-Analyzer allows for the existence of a small amount of undesired sequences as a tradeoff between the number of degenerate primers and the encoded library size while still providing all the benefits of DC-Analyzer with the ability to randomize multiple contiguous sites. MDC-Analyzer was validated using a series of randomly generated mutation schemes and experimental case studies on the evolution of halohydrin dehalogenase, which proved that the MDC methodology is more efficient than other methods and is particularly well-suited to exploring the sequence space of proteins using data-driven protein engineering strategies.

Drug resistance missense mutations in cancer are subject to evolutionary constraints

Several tumour types are sensitive to deactivation of just one or very few genes that are constantly active in the cancer cells, a phenomenon that is termed ‘oncogene addiction’. Drugs that target the products of those oncogenes can yield a temporary relief, and even complete remission. Unfortunately, many patients receiving oncogene-targeted therapies relapse on treatment. This often happens due to somatic mutations in the oncogene (‘resistance mutations’). ‘Compound mutations’, which in the context of cancer drug resistance are defined as two or more mutations of the drug target in the same clone may lead to enhanced resistance against the most selective inhibitors. Here, it is shown that the vast majority of the resistance mutations occurring in cancer patients treated with tyrosin kinase inhibitors aimed at three different proteins follow an evolutionary pathway. Using bioinformatic analysis tools, it is found that the drug-resistance mutations in the tyrosine kinase domains of Abl1, ALK and exons 20 and 21 of EGFR favour transformations to residues that can be identified in similar positions in evolutionary related proteins. The results demonstrate that evolutionary pressure shapes the mutational landscape in the case of drug-resistance somatic mutations. The constraints on the mutational landscape suggest that it may be possible to counter single drug-resistance point mutations. The observation of relatively many resistance mutations in Abl1, but not in the other genes, is explained by the fact that mutations in Abl1 tend to be biochemically conservative, whereas mutations in EGFR and ALK tend to be radical. Analysis of Abl1 compound mutations suggests that such mutations are more prevalent than hitherto reported and may be more difficult to counter. This supports the notion that such mutations may provide an escape route for targeted cancer drug resistance.

Structure-based Methods for Computational Protein Functional Site Prediction

Due to the advent of high throughput sequencing techniques and structural genomic projects, the number of gene and protein sequences has been ever increasing. Computational methods to annotate these genes and proteins are even more indispensable. Proteins are important macromolecules and study of the function of proteins is an important problem in structural bioinformatics. This paper discusses a number of methods to predict protein functional site especially focusing on protein ligand binding site prediction. Initially, a short overview is presented on recent advances in methods for selection of homologous sequences. Furthermore, a few recent structural based approaches and sequence-and-structure based approaches for protein functional sites are discussed in details.

Mini-chromosome maintenance complexes form a filament to remodel DNA structure and topology

Deregulation of mini-chromosome maintenance (MCM) proteins is associated with genomic instability and cancer. MCM complexes are recruited to replication origins for genome duplication. Paradoxically, MCM proteins are in excess than the number of origins and are associated with chromatin regions away from the origins during G1 and S phases. Here, we report an unusually wide left-handed filament structure for an archaeal MCM, as determined by X-ray and electron microscopy. The crystal structure reveals that an α-helix bundle formed between two neighboring subunits plays a critical role in filament formation. The filament has a remarkably strong electro-positive surface spiraling along the inner filament channel for DNA binding. We show that this MCM filament binding to DNA causes dramatic DNA topology change. This newly identified function of MCM to change DNA topology may imply a wider functional role for MCM in DNA metabolisms beyond helicase function. Finally, using yeast genetics, we show that the inter-subunit interactions, important for MCM filament formation, play a role for cell growth and survival.

Protein interface classification by evolutionary analysis

Background

Distinguishing biologically relevant interfaces from lattice contacts in protein crystals is a fundamental problem in structural biology. Despite efforts towards the computational prediction of interface character, many issues are still unresolved.

Results

We present here a protein-protein interface classifier that relies on evolutionary data to detect the biological character of interfaces. The classifier uses a simple geometric measure, number of core residues, and two evolutionary indicators based on the sequence entropy of homolog sequences. Both aim at detecting differential selection pressure between interface core and rim or rest of surface. The core residues, defined as fully buried residues (>95% burial), appear to be fundamental determinants of biological interfaces: their number is in itself a powerful discriminator of interface character and together with the evolutionary measures it is able to clearly distinguish evolved biological contacts from crystal ones. We demonstrate that this definition of core residues leads to distinctively better results than earlier definitions from the literature. The stringent selection and quality filtering of structural and sequence data was key to the success of the method. Most importantly we demonstrate that a more conservative selection of homolog sequences - with relatively high sequence identities to the query - is able to produce a clearer signal than previous attempts.

Conclusions

An evolutionary approach like the one presented here is key to the advancement of the field, which so far was missing an effective method exploiting the evolutionary character of protein interfaces. Its coverage and performance will only improve over time thanks to the incessant growth of sequence databases. Currently our method reaches an accuracy of 89% in classifying interfaces of the Ponstingl 2003 datasets and it lends itself to a variety of useful applications in structural biology and bioinformatics. We made the corresponding software implementation available to the community as an easy-to-use graphical web interface at http://www.eppic-web.org.

A rigid network of long-range contacts increases thermostability in a mutant endoglucanase

Thermodynamic stability of a protein at elevated temperatures is a key factor for thermostable enzymes to catalyze their specific reactions. Yet our understanding of biological determinants of thermostability is far from complete. Many different atomistic factors have been suggested as possible means for such proteins to preserve their activity at high temperatures. Among these factors are specific local interatomic interactions or enrichment of specific amino acid types. The case of glycosyl hydrolase family endoglucanase of Trichoderma reesei defies current hypotheses for thermostability because a single mutation far from the active site (A35 V) converts this mesostable protein into a thermostable protein without significant change in the protein structure. This substantial change in enzymatic activity cannot be explained on the basis of local intramolecular interactions alone. Here we present a more global view of the induced thermostability and show that the A35 V mutation affects the underlying structural rigidity of the whole protein via a number of long-range, non-local interactions. Our analysis of this structure reveals a precisely tuned, rigid network of atomic interactions. This cooperative, allosteric effect promotes the transformation of this mesostable protein into a thermostable one.

Protein-protein interactions in clathrin vesicular assembly: radial distribution of evolutionary constraints in interfaces

In eukaryotic organisms clathrin-coated vesicles are instrumental in the processes of endocytosis as well as intracellular protein trafficking. Hence, it is important to understand how these vesicles have evolved across eukaryotes, to carry cargo molecules of varied shapes and sizes. The intricate nature and functional diversity of the vesicles are maintained by numerous interacting protein partners of the vesicle system. However, to delineate functionally important residues participating in protein-protein interactions of the assembly is a daunting task as there are no high-resolution structures of the intact assembly available. The two cryoEM structures closely representing intact assembly were determined at very low resolution and provide positions of Cα atoms alone. In the present study, using the method developed by us earlier, we predict the protein-protein interface residues in clathrin assembly, taking guidance from the available low-resolution structures. The conservation status of these interfaces when investigated across eukaryotes, revealed a radial distribution of evolutionary constraints, i.e., if the members of the clathrin vesicular assembly can be imagined to be arranged in spherical manner, the cargo being at the center and clathrins being at the periphery, the detailed phylogenetic analysis of these members of the assembly indicated high-residue variation in the members of the assembly closer to the cargo while high conservation was noted in clathrins and in other proteins at the periphery of the vesicle. This points to the strategy adopted by the nature to package diverse proteins but transport them through a highly conserved mechanism.

The non-random clustering of non-synonymous substitutions and its relationship to evolutionary rate

Background

Protein sequences are subject to a mosaic of constraint. Changes to functional domains and buried residues, for example, are more apt to disrupt protein structure and function than are changes to residues participating in loops or exposed to solvent. Regions of constraint on the tertiary structure of a protein often result in loose segmentation of its primary structure into stretches of slowly- and rapidly-evolving amino acids. This clustering can be exploited, and existing methods have done so by relying on local sequence conservation as a signature of selection to help identify functionally important regions within proteins. We invert this paradigm by leveraging the regional nature of protein structure and function to both illuminate and make use of genome-wide patterns of local sequence conservation.

Results

Our hypothesis is that the regional nature of structural and functional constraints will assert a positive autocorrelation on the evolutionary rates of neighboring sites, which, in a pairwise comparison of orthologous proteins, will manifest itself as the clustering of non-synonymous changes across the amino acid sequence. We introduce a dispersion ratio statistic to test this and related hypotheses. Using genome-wide interspecific comparisons of orthologous protein pairs, we reveal a strong log-linear relationship between the degree of clustering and the intensity of constraint. We further demonstrate how this relationship varies with the evolutionary distance between the species being compared. We provide some evidence that proteins with a history of positive selection deviate from genome-wide trends.

Conclusions

We find a significant association between the evolutionary rate of a protein and the degree to which non-synonymous changes cluster along its primary sequence. We show that clustering is a non-redundant predictor of evolutionary rate, and we speculate that conflicting signals of clustering and constraint may be indicative of a historical period of relaxed selection.

Protein function prediction: towards integration of similarity metrics

Genomic centers discover increasingly many protein sequences and structures, but not necessarily their full biological functions. Thus, currently, less than one percent of proteins have experimentally verified biochemical activities. To fill this gap, function prediction algorithms apply metrics of similarity between proteins on the premise that those sufficiently alike in sequence, or structure, will perform identical functions. Although high sensitivity is elusive, network analyses that integrate these metrics together hold the promise of rapid gains in function prediction specificity.

Protein structure databases

Web-based protein structure databases come in a wide variety of types and levels of information content. Those having the most general interest are the various atlases that describe each experimentally determined protein structure and provide useful links, analyses and schematic diagrams relating to its 3D structure and biological function. Also of great interest are the databases that classify 3D structures by their folds as these can reveal evolutionary relationships which may be hard to detect from sequence comparison alone. Related to these are the numerous servers that compare folds-particularly useful for newly solved structures, and especially those of unknown function. Beyond these there are a vast number of databases for the most specialized user, dealing with specific families, diseases, structural features and so on.

The LabelHash algorithm for substructure matching

Background

There is an increasing number of proteins with known structure but unknown function. Determining their function would have a significant impact on understanding diseases and designing new therapeutics. However, experimental protein function determination is expensive and very time-consuming. Computational methods can facilitate function determination by identifying proteins that have high structural and chemical similarity.

Results

We present LabelHash, a novel algorithm for matching substructural motifs to large collections of protein structures. The algorithm consists of two phases. In the first phase the proteins are preprocessed in a fashion that allows for instant lookup of partial matches to any motif. In the second phase, partial matches for a given motif are expanded to complete matches. The general applicability of the algorithm is demonstrated with three different case studies. First, we show that we can accurately identify members of the enolase superfamily with a single motif. Next, we demonstrate how LabelHash can complement SOIPPA, an algorithm for motif identification and pairwise substructure alignment. Finally, a large collection of Catalytic Site Atlas motifs is used to benchmark the performance of the algorithm. LabelHash runs very efficiently in parallel; matching a motif against all proteins in the 95% sequence identity filtered non-redundant Protein Data Bank typically takes no more than a few minutes. The LabelHash algorithm is available through a web server and as a suite of standalone programs at http://labelhash.kavrakilab.org. The output of the LabelHash algorithm can be further analyzed with Chimera through a plugin that we developed for this purpose.

Conclusions

LabelHash is an efficient, versatile algorithm for large-scale substructure matching. When LabelHash is running in parallel, motifs can typically be matched against the entire PDB on the order of minutes. The algorithm is able to identify functional homologs beyond the twilight zone of sequence identity and even beyond fold similarity. The three case studies presented in this paper illustrate the versatility of the algorithm.

Phospho3D 2.0: an enhanced database of three-dimensional structures of phosphorylation sites

Phospho3D is a database of three-dimensional (3D) structures of phosphorylation sites (P-sites) derived from the Phospho.ELM database, which also collects information on the residues surrounding the P-site in space (3D zones). The database also provides the results of a large-scale structural comparison of the 3D zones versus a representative dataset of structures, thus associating to each P-site a number of structurally similar sites. The new version of Phospho3D presents an 11-fold increase in the number of 3D sites and incorporates several additional features, including new structural descriptors, the possibility of selecting non-redundant sets of 3D structures and the availability for download of non-redundant sets of structurally annotated P-sites. Moreover, it features P3Dscan, a new functionality that allows the user to submit a protein structure and scan it against the 3D zones collected in the Phospho3D database. Phospho3D version 2.0 is available at: http://www.phospho3d.org/.

Heme proteins--diversity in structural characteristics, function, and folding

The characteristics of heme prosthetic groups and their binding sites have been analyzed in detail in a data set of nonhomologous heme proteins. Variations in the shape, volume, and chemical composition of the binding site, in the mode of heme binding and in the number and nature of heme-protein interactions are found to result in significantly different heme environments in proteins with different functions in biology. Differences are also seen in the properties of the apo states of the proteins. The apo states of proteins that bind heme permanently in their functional form show some disorder, ranging from local unfolding in the heme binding pocket to complete unfolding to give a random coil. In contrast, proteins that bind heme transiently are fully folded in their apo and holo states, presumably allowing both apo and holo forms to remain biologically active resisting aggregation or proteolysis. The principles identified here provide a framework for the design of de novo proteins that will exhibit tight heme ligand binding and for the identification of the function of structural genomic target proteins with heme ligands.

Prediction of protein long-range contacts using an ensemble of genetic algorithm classifiers with sequence profile centers

Background

Prediction of long-range inter-residue contacts is an important topic in bioinformatics research. It is helpful for determining protein structures, understanding protein foldings, and therefore advancing the annotation of protein functions.

Results

In this paper, we propose a novel ensemble of genetic algorithm classifiers (GaCs) to address the long-range contact prediction problem. Our method is based on the key idea called sequence profile centers (SPCs). Each SPC is the average sequence profiles of residue pairs belonging to the same contact class or non-contact class. GaCs train on multiple but different pairs of long-range contact data (positive data) and long-range non-contact data (negative data). The negative data sets, having roughly the same sizes as the positive ones, are constructed by random sampling over the original imbalanced negative data. As a result, about 21.5% long-range contacts are correctly predicted. We also found that the ensemble of GaCs indeed makes an accuracy improvement by around 5.6% over the single GaC.

Conclusions

Classifiers with the use of sequence profile centers may advance the long-range contact prediction. In line with this approach, key structural features in proteins would be determined with high efficiency and accuracy.

ProBiS algorithm for detection of structurally similar protein binding sites by local structural alignment

Motivation: Exploitation of locally similar 3D patterns of physicochemical properties on the surface of a protein for detection of binding sites that may lack sequence and global structural conservation.

Results: An algorithm, ProBiS is described that detects structurally similar sites on protein surfaces by local surface structure alignment. It compares the query protein to members of a database of protein 3D structures and detects with sub-residue precision, structurally similar sites as patterns of physicochemical properties on the protein surface. Using an efficient maximum clique algorithm, the program identifies proteins that share local structural similarities with the query protein and generates structure-based alignments of these proteins with the query. Structural similarity scores are calculated for the query protein's surface residues, and are expressed as different colors on the query protein surface. The algorithm has been used successfully for the detection of protein–protein, protein–small ligand and protein–DNA binding sites.

Availability: The software is available, as a web tool, free of charge for academic users at http://probis.cmm.ki.si

Contact:dusa@cmm.ki.si

Supplementary information:Supplementary data are available at Bioinformatics online.