CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods

BACKGROUND

The Critical Assessment of Genome Interpretation (CAGI) aims to advance the state-of-the-art for computational prediction of genetic variant impact, particularly where relevant to disease. The five complete editions of the CAGI community experiment comprised 50 challenges, in which participants made blind predictions of phenotypes from genetic data, and these were evaluated by independent assessors.

RESULTS

Performance was particularly strong for clinical pathogenic variants, including some difficult-to-diagnose cases, and extends to interpretation of cancer-related variants. Missense variant interpretation methods were able to estimate biochemical effects with increasing accuracy. Assessment of methods for regulatory variants and complex trait disease risk was less definitive and indicates performance potentially suitable for auxiliary use in the clinic.

CONCLUSIONS

Results show that while current methods are imperfect, they have major utility for research and clinical applications. Emerging methods and increasingly large, robust datasets for training and assessment promise further progress ahead.

"Redirecting an anti-IL-1β antibody to bind a new, unrelated and computationally predicted epitope on hIL-17A"

Antibody engineering technology is at the forefront of therapeutic antibody development. The primary goal for engineering a therapeutic antibody is the generation of an antibody with a desired specificity, affinity, function, and developability profile. Mature antibodies are considered antigen specific, which may preclude their use as a starting point for antibody engineering. Here, we explore the plasticity of mature antibodies by engineering novel specificity and function to a pre-selected antibody template. Using a small, focused library, we engineered AAL160, an anti-IL-1β antibody, to bind the unrelated antigen IL-17A, with the introduction of seven mutations. The final redesigned antibody, 11.003, retains favorable biophysical properties, binds IL-17A with sub-nanomolar affinity, inhibits IL-17A binding to its cognate receptor and is functional in a cell-based assay. The epitope of the engineered antibody can be computationally predicted based on the sequence of the template antibody, as is confirmed by the crystal structure of the 11.003/IL-17A complex. The structures of the 11.003/IL-17A and the AAL160/IL-1β complexes highlight the contribution of germline residues to the paratopes of both the template and re-designed antibody. This case study suggests that the inherent plasticity of antibodies allows for re-engineering of mature antibodies to new targets, while maintaining desirable developability profiles.

Accurate profiling of full-length Fv in highly homologous antibody libraries using UMI tagged short reads

Deep parallel sequencing (NGS) is a viable tool for monitoring scFv and Fab library dynamics in many antibody engineering high-throughput screening efforts. Although very useful, the commonly used Illumina NGS platform cannot handle the entire sequence of scFv or Fab in a single read, usually focusing on specific CDRs or resorting to sequencing VH and VL variable domains separately, thus limiting its utility in comprehensive monitoring of selection dynamics. Here we present a simple and robust method for deep sequencing repertoires of full length scFv, Fab and Fv antibody sequences. This process utilizes standard molecular procedures and unique molecular identifiers (UMI) to pair separately sequenced VH and VL. We show that UMI assisted VH-VL matching allows for a comprehensive and highly accurate mapping of full length Fv clonal dynamics in large highly homologous antibody libraries, as well as identification of rare variants. In addition to its utility in synthetic antibody discovery processes, our method can be instrumental in generating large datasets for machine learning (ML) applications, which in the field of antibody engineering has been hampered by conspicuous paucity of large scale full length Fv data.

DistilProtBert: a distilled protein language model used to distinguish between real proteins and their randomly shuffled counterparts

SUMMARY

Recently, deep learning models, initially developed in the field of natural language processing (NLP), were applied successfully to analyze protein sequences. A major drawback of these models is their size in terms of the number of parameters needed to be fitted and the amount of computational resources they require. Recently, 'distilled' models using the concept of student and teacher networks have been widely used in NLP. Here, we adapted this concept to the problem of protein sequence analysis, by developing DistilProtBert, a distilled version of the successful ProtBert model. Implementing this approach, we reduced the size of the network and the running time by 50%, and the computational resources needed for pretraining by 98% relative to ProtBert model. Using two published tasks, we showed that the performance of the distilled model approaches that of the full model. We next tested the ability of DistilProtBert to distinguish between real and random protein sequences. The task is highly challenging if the composition is maintained on the level of singlet, doublet and triplet amino acids. Indeed, traditional machine-learning algorithms have difficulties with this task. Here, we show that DistilProtBert preforms very well on singlet, doublet and even triplet-shuffled versions of the human proteome, with AUC of 0.92, 0.91 and 0.87, respectively. Finally, we suggest that by examining the small number of false-positive classifications (i.e. shuffled sequences classified as proteins by DistilProtBert), we may be able to identify de novo potential natural-like proteins based on random shuffling of amino acid sequences.

AVAILABILITY AND IMPLEMENTATION

https://github.com/yarongef/DistilProtBert.

Trial in progress: A phase 1-2, first-in-human, open label, dose escalation and expansion study of AU-007, a monoclonal antibody that binds to IL-2 and inhibits IL-2Rα binding, in patients with advanced solid tumors

TPS2671

Background: AU-007 is a computationally designed, monoclonal antibody that binds to IL-2 on its CD25 binding epitope. AU-007 bound IL-2 (A/IL-2) cannot bind to high affinity trimeric IL-2 receptors (IL-2R) consisting of CD25, CD122, and CD132 expressed on Tregs and vascular endothelium, but its binding to low affinity dimeric IL-2Rs (CD122 and CD132) expressed on T effector and NK cells is unhindered. Thus, AU-007 redirects endogenously produced or exogenous IL-2 (aldesleukin) towards activation of immune stimulating T effector and NK cells, while diminishing Treg activation and expansion. AU-007 will also bind and redirect newly secreted endogenous IL-2 resulting from A/IL-2 driven T cell expansion in the tumor, converting a Treg mediated autoinhibitory loop into an immune stimulating loop. AU-007 is unique in the IL-2 therapeutic field as engineered exogenous, recombinant “non-CD25” IL-2s in development cannot address the autoinhibitory effect of endogenous IL-2. Preclinically, AU-007 has been demonstrated to capture endogenous human IL-2 in vivo. AU-007 with a single low dose of IL-2 has demonstrated efficacy in multiple cancer models and has an excellent safety profile in non-human primates. Methods: This first-in-human, multicenter, open label Phase 1- 2 study evaluates the safety, tolerability, and initial efficacy of AU-007 +/- aldesleukin in patients with advanced solid tumors (CT-2021-CTN-03938-1). Phase 1 consists of 3 escalation arms each starting with a single 1+2 escalation cohort followed by 3+3 escalation cohorts to define the recommended Phase 2 dose (RP2D) or maximum tolerated dose (MTD). Patients with melanoma, renal cell carcinoma (RCC) and 17 selected solid tumors are eligible. Prior treatment with check point inhibitors is allowed. In Arm A, escalating doses of Q2w AU-007 are evaluated in sequential escalation cohorts. In Arm B, a single dose of aldesleukin is given with the initial AU-007 dose. AU-007 is given at a fixed dose Q2w with an escalating single aldesleukin dose in sequential escalation cohorts. In Arm C, AU-007 is evaluated in combination with aldesleukin, both given Q2w. AU-007 is administered at a fixed dose with an escalating dose of aldesleukin in sequential cohorts. The Phase 2 cohort expansion portion of the study evaluates the initial efficacy at the RP2D defined in escalation cohorts A, B, and C in 3 matching expansion cohorts of up to 20 patients each. Patients with advanced melanoma, RCC and other tumors including, but not limited to, Merkel Cell Carcinoma, NSCLC, and urothelial cancer are eligible. Enrolment to the study commences in Australia, with US sites planned to open later in 2022. Clinical trial information: CT-2021-CTN-03938-1.

704 The computationally designed human antibody, AU-007, mediates human immune activation by endogenous IL-2, while uniquely breaking the IL-2 auto-inhibitory loop and preventing Treg expansion

Background

IL-2 binds two forms of IL-2 receptor: a high affinity trimeric receptor composed of CD25, CD122, and CD132, and a low affinity dimeric receptor composed of CD122 and CD132. Binding to the dimeric receptors, expressed on effector cells, causes expansion of the effector arm of the immune system including CD8 T-cells, NK-cells and NKT-cells. Binding to the trimeric receptor, expressed on Tregs as well as on pulmonary and vascular epithelium, results in expansion of Treg cells and Vascular Leak Syndrome, both are undesired outcomes of high-dose recombinant IL-2 (Aldesleukin), approved for treatment of Melanoma and Renal-Cell-Carcinoma.

Methods

Flow-cytometry analysis of immune-cell populations of C57BL/6 mice and hPBMCs. Tumor-Growth-Index of murine cancer models.

Results

AU-007, is a computationally designed human antibody that bind the CD25-binding portion on IL-2, preventing binding of IL-2 to the trimeric receptor, but not the dimeric receptor. This leads to immune effector activation while also preventing the Treg expansion via the autoinhibitory loop caused by endogenous IL-2 secreted from activated T effector cells (figure 1). AU-007 binds human IL-2 with picomolar affinity and has excellent biophysical properties with low potential for anti-drug immunogenicity (figure 2). Administration of an AU-007/low dose hIL-2 complex to non-tumor bearing C57BL/6 mice promoted proliferation of effector cells with no effect on Tregs (figure 3). Additionally, an AU-007/low dose hIL-2 complex was highly effective in inhibiting tumor progression in a syngeneic B16F10 melanoma model (figure 4). pSTAT5 analysis of hPBMCs incubated with AU-007 and hIL-2 demonstrated activation of the effector cells and inhibition of Tregs expansion (figure 5). hPBMCs activated with anti-CD3/anti-CD28 and treated with either AU-007 or an isotype control antibody but without exogenous IL-2, showed expansion of effector cells. However, while the isotype control antibody expanded also Tregs , AU-007 inhibited Tregs proliferation, indicating that AU-007 captures endogenous IL-2 and prevents the Treg expansion autoinhibitory loop caused by endogenous IL-2 secreted from activated T effector cells (figure 6).Additionally, following establishment of the IL-2 auto-secretion feedback loop in mice genetically engineered to express hIL-2 instead of murine IL-2, AU-007 treatment significantly inhibited MC38 colorectal-tumor growth for twelve days, in a manner comparable to treatment with anti-PD1 (figure 7).

Conclusions

AU-007 is a human antibody that blocks the CD25-binding epitope on IL-2. It redirects endogenous IL-2 to promote effector cell expansion while simultaneously blocking the Treg expansion autoinhibitory loop, indicating its unique therapeutic profile and high potential as a novel cancer treatment. AU-007 is expected to enter clinical testing in 2021.

Abstract 704 Figure 1

Schematic representation of IL-2 mechanism of action and its dual role in controlling immune response. IL-2 structure consists of three binding epitope sites that interact with different forms of IL-2-R complexes with different affinities (Left Panel). IL-2R complexes expressed on different cell populations and their different affinities to IL-2 allow immunosuppression under conditions of low local concentrations of IL-2 and immune stimulation when IL-2 local concentration rises (middle panel). Au-007 utilize autocrine human IL-2 MOA to promote immune stimulation. Targeting IL-2 to different cell populations can be used to modulate the immune response toward towards immune activation. An anti-human IL-2 antibody tumor clearance while reducing IL-2's undesired interactions with endothelial CD25 expressing cells preventing IL-2 induced pulmonary edema and vascular leaking.

Abstract 704 Figure 2

Au-007 bind human IL-2 with high affinity and inhibits the binding to CD25 while preserving the binding to CD122. Affinity and binding site are demonstrated using Surface Plasmon Resonance. Au-007 was captured on CM5 chip and soluble hIL-2 was injected, forming a complex. Subsequently, soluble CD25 was injected followed by injection of soluble CD122 (A). SPR trace of complex formation of Ab/IL-2/IL-2R arrows indicate where Au-007 (17.069), hIL-2, CD25 and CD122 were injected (B). SPR trace and calculated binding kinetics of chip bound Au-007 with hIL-2 serving as analyte (C). Biophysical profile of Au-007. Au-007 was subjected to five freeze thaw cycles, agitation for 3 days and incubation at 40°C for 1 week. Post treatment Au-007 integrity and indicated biophysical properties were measured (D).

Abstract 704 Figure 3

Au-007 demonstrated in-vivo potent immune stimulating effects in a dose depended manner, with no observed effect on Tregs. C57BL/6 healthy mice were administered daily with Au-007/hIL-2 complex for four days. On day five splenocytes were isolated and immune cells populations were analyzed using flow cytometry. (A) Dosing regimen outline. (B–E) Mean values of immune cells calculated as a percentage from parent population of each experimental group (n=6 per group)

Abstract 704 Figure 4

Au-007 inhibits tumor growth in an I/O resistant tumor model with a tolerable profile. C57BL/6 healthy mice were inoculated with B16F10 melanoma tumor cells (day 0), at day 5 mice were randomized to experimental groups (n=10 per group) and administered daily, with single injection per day of Ab/hIL-2 mix (20 ug/1 ug respectively) or with PBS for four days. From the end of schedule administration at day 8 until experiment endpoint, mice were monitored daily for tumor volume (A) and for mean percent of body weight change for each experimental group (B).

Abstract 704 Figure 5

AU-007 inhibits the effect of IL-2 on Tregs while preserving its effect on Teffs and NKs. (A and B) Phosphorylated STAT5 levels of human immune cell subsets responding to various concentrations of hIL-2 with and without 200 nM AU-007. Total naïve hPBMC culture were incubated with increasing doses of hIL-2 or with increasing doses of hIL-2 + 200nM AU-007 for 15 min. Immune cells subpopulations were analyzed by flow cytometry, gating was defined based on FMOs. (C–F) Phosphorylated STAT5 levels of human immune cell subsets responding to titrated AU-007 or isotype control. Total naïve hPBMC culture were incubated with hIL-2 and with increasing doses of indicated antibody for 15 min. Data presented is an average of 3 biological repeats from 3 human PBMC donors.

Abstract 704 Figure 6

Au-007 can rely on endogenous IL-2 to break auto-inhibitory loop in human PBMCs. Total hPBMCs were stimulated for 24h with anti-CD3/anti-CD28 (stimulation only, green) or stimulated with anti-CD3/anti-CD28 in the presence of: 200 nM of Au-007 mAb (red) or with 200 nM of isotype control mAb (blue). No exogenous IL-2 was added. Immune cells subpopulations were analyzed by flow cytometry. Percentage of immune cell sub-populations demonstrate exclusive inhibition of Tregs (A–E). Au-007 downregulate the suppressive markers of CD4+ regulatory Tregs from panel A, as defined by significant reduction in MFI of CD25 and FoxP3 (F and G). Gating was defined based on FMOs. Data presented is an average of 3 biological repeats from 3 human PBMC donors.

Abstract 704 Figure 7

Au-007 captures endogenous hIL-2 and inhibits tumor growth in colorectal cancer model (MC38). Genetically modified C57BL/6 mice, engineered to express human IL-2 in the background of complete knock-out of mouse IL-2, were inoculated with MC38 colorectal tumor cells. All animals treated with Au-007 showed significant inhibition in tumor growth with no observed significant adverse effects. (A) Administration outline: PBS (black), anti-mouse-PD-1 antibody (yellow), Au-007 pre-complexed with low dose IL-2 (blue) and Au-007 alone every three days followed with a single immune kick start with IL-2 (green, IL-2 single dose is marked in red). (B) Tumor growth progression of the four groups treated. (C) Percent of body weight changes per treatment.

Characterization of BDG8: An antibody-human IL-2 complex that selectively activates the effector arm of the immune system

e15004

Background: Interleukin 2 is a 15.4 kDa type I cytokine of the four helix bundle structure. IL-2 signaling has two opposite effects, it can enhance immune response by activation of effector cells and regulate immune response by activation proliferation of regulatory T cells (Tregs). IL-2 mediates its effect by binding to two forms of IL-2 receptor: i) trimeric receptors made of IL-2Rα (CD25), IL-2Rβ (CD122), and a common IL-2Rγ (γc, CD132) chains or ii) a dimeric receptor of only the IL-2Rβ and IL-2Rγ subunits. In Tregs, activation of the trimeric receptor is associated with FoxP3 mediated transcription leading to the production of suppression factors. Tregs express higher levels of the αβγ trimeric IL-2 receptor than effector cells. However binding of IL-2 to the βγ dimer is associated with activation of effector cells expressing relatively high levels of the βγ dimer and express low levels of trimer. High dose IL-2 therapy is used in melanoma and metastatic renal cell carcinoma with response rates 10%-15%. While efficacious, this approach has several disadvantages: 1) IL-2 dependent adverse effects such as Vascular Leak Syndrome (VLS) excluding many patients from being considered 2) The short half-life of IL-2 requires frequent administration, resulting in repeated spikes in the level of circulating IL-2. 3) Administered IL-2 is not selective and enhances a non-desired activation of Tregs. Methods: Antibodies were computational designed to bind to a specific epitope on Il-2 allowing for the binding to the CD122/CD132 complex and exclude the CD122/CD132/CD25 complex. Results: Here we show that we specifically designed and tested a humanized antibody (BD8) that: i) binds tightly the human IL-2 (hIL-2) and ii) the antibody- hIL-2 complex preferentially to bind the IL-2Rβ sub-unit and blocks the IL-2Rα subunit. We show administering the BD8 Ab-IL-2 complex to C57BL/6 mice has a profound effect on activation of MP CD8+, NK and NKT effector cells with minimal effect on Tregs. In mice the antibody-hIL-2 complex serum half -life is much longer then the half-life of hIL-2. In the B16-F10 Melanoma model, BD8-hIL2 resulted in robust effect compared to hIL-2 alone. Conclusions: BD8 is a computationally designed antibody specifically binds to IL-2 inducing the activation of effector cells without stimulating Tregs and demonstrates efficacy in animal cancer models.

Anti-herpesvirus prophylaxis, pre-emptive treatment or no treatment in adults undergoing allogeneic transplant for haematological disease: systematic review and meta-analysis

BACKGROUND

Herpesviridae infections incur significant morbidity and indirect effects on mortality among allogeneic haematopoietic cell transplant (allo-HCT) recipients.

OBJECTIVES

To study the effects of antiviral prevention strategies among haemato-oncological individuals undergoing allo-HCT.

DATA SOURCES

Cochrane Central Register of Controlled Trials, MEDLINE, Embase and LILACS. We further searched for conference proceedings and trial registries.

STUDY ELIGIBILITY CRITERIA

Randomized controlled trials (RCTs).

PARTICIPANTS

Adults with haematological malignancy undergoing allo-HCT.

INTERVENTIONS

Antiviral prophylaxis versus no treatment/placebo or pre-emptive treatment and pre-emptive treatment versus prophylaxis with the same agent.

METHODS

Random-effects meta-analysis was conducted computing pooled risk ratios (RR) with 95% CI and the inconsistency measure (I²). The certainty of the evidence was appraised by GRADE.

RESULTS

We included 22 RCTs. Antiviral prophylaxis reduced all-cause mortality (RR 0.83, 95% CI 0.7-0.99; 15 trials, I² = 0%), cytomegalovirus (CMV) disease (RR 0.54, 95% CI 0.34-0.85; n = 15, I² = 20%) and herpes simplex virus (HSV) disease (RR 0.29, 95% CI 0.2-0.43; n = 13, I² = 18%) compared with no treatment/placebo or pre-emptive treatment, all with high-certainty evidence. Furthermore, antivirals reduced HSV infection, CMV pneumonitis, CMV infection and varicella zoster virus disease. Anti-CMV prophylaxis (+/- pre-emptive treatment) compared with pre-emptive treatment alone reduced non-significantly all-cause mortality (RR 0.78, 95% CI 0.6-1.02; n = 8, I² = 0%), CMV disease (RR 0.47, 95% CI 0.23-0.97; n = 9, I² = 30%) and HSV disease (RR 0.41, 95% CI 0.24-0.67; n = 4, I² = 0%) with high-certainty evidence, as well as CMV and HSV infections. Antiviral prophylaxis did not result in increased adverse event rates overall or more discontinuation due to adverse events.

CONCLUSIONS

Antiviral prophylaxis directed against herpesviruses is highly effective and safe, reducing mortality, HSV and CMV disease, as well as herpesvirus reactivations among allo-HCT recipients. Anti-CMV prophylaxis is more effective than pre-emptive treatment alone with respect to HSV and CMV disease and infection.

A novel Fer/FerT targeting compound selectively evokes metabolic stress and necrotic death in malignant cells

Disruption of the reprogrammed energy management system of malignant cells is a prioritized goal of targeted cancer therapy. Two regulators of this system are the Fer kinase, and its cancer cell specific variant, FerT, both residing in subcellular compartments including the mitochondrial electron transport chain. Here, we show that a newly developed inhibitor of Fer and FerT, E260, selectively evokes metabolic stress in cancer cells by imposing mitochondrial dysfunction and deformation, and onset of energy-consuming autophagy which decreases the cellular ATP level. Notably, Fer was also found to associate with PARP-1 and E260 disrupted this association thereby leading to PARP-1 activation. The cooperative intervention with these metabolic pathways leads to energy crisis and necrotic death in malignant, but not in normal human cells, and to the suppression of tumors growth in vivo. Thus, E260 is a new anti-cancer agent which imposes metabolic stress and cellular death in cancer cells.

The tyrosine-kinases Fer/FerT associate with the mitochondrial electron transport chain in cancer cells supporting their metabolic reprogramming. Here the authors discover a compound that disrupts Fer /FerT activity and selectively induces cell death of cancer cell lines displaying anti-tumor activity in vivo.

Working toward precision medicine: Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges

Precision medicine aims to predict a patient's disease risk and best therapeutic options by using that individual's genetic sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype-phenotype prediction challenges; participants build models, undergo assessment, and share key findings. For CAGI 4, three challenges involved using exome-sequencing data: Crohn's disease, bipolar disorder, and warfarin dosing. Previous CAGI challenges included prior versions of the Crohn's disease challenge. Here, we discuss the range of techniques used for phenotype prediction as well as the methods used for assessing predictive models. Additionally, we outline some of the difficulties associated with making predictions and evaluating them. The lessons learned from the exome challenges can be applied to both research and clinical efforts to improve phenotype prediction from genotype. In addition, these challenges serve as a vehicle for sharing clinical and research exome data in a secure manner with scientists who have a broad range of expertise, contributing to a collaborative effort to advance our understanding of genotype-phenotype relationships.

The evolving role of chemotherapy and hematopoietic cell transplants in Ph-positive acute lymphoblastic leukemia in adults

The introduction of the tyrosine kinase inhibitors (TKI) into the treatment of patients with Ph or BCR-ABL1-positive acute lymphoblastic leukemia has revolutionized the treatment of this poor prognosis acute leukemia. The combination of TKI with chemotherapy has improved response rates and allowed more patients to proceed to allogeneic hematopoietic cell transplant (alloHCT). Older patients have excellent responses to TKI and corticosteroids or in combination with minimal chemotherapy. This raises the question as to whether patients require full-intensity chemotherapy with TKI to achieve molecular remissions. The pediatricians have proposed that cure is achievable without alloHCT in children. These results have suggested that many patients may not require traditional chemotherapy in addition to TKI to achieve remission, and that patients who achieve a negative minimal residual disease state may not require alloHCT. The data in support of these questions is presented here and a suggested future clinical trial design based on these data is proposed.

How far from the SNP may the causative genes be?

While GWAS identify many disease-associated SNPs, using them to decipher disease mechanisms is hindered by the difficulty in mapping SNPs to genes. Most SNPs are in non-coding regions and it is often hard to identify the genes they implicate. To explore how far the SNP may be from the affected genes we used a pathway-based approach. We found that affected genes are often up to 2 Mbps away from the associated SNP, and are not necessarily the closest genes to the SNP. Existing approaches for mapping SNPs to genes leave many SNPs unmapped to genes and reveal only 86 significant phenotype-pathway associations for all known GWAS hits combined. Using the pathway-based approach we propose here allows mapping of virtually all SNPs to genes and reveals 435 statistically significant phenotype-pathway associations. In search for mechanisms that may explain the relationships between SNPs and distant genes, we found that SNPs that are mapped to distant genes have significantly more large insertions/deletions around them than other SNPs, suggesting that these SNPs may sometimes be markers for large insertions/deletions that may affect large genomic regions.

Understanding differences between synthetic and natural antibodies can help improve antibody engineering

Synthetic libraries are a major source of human-like antibody (Ab) drug leads. To assess the similarity between natural Abs and the products of these libraries, we compared large sets of natural and synthetic Abs using "CDRs Analyzer," a tool we introduce for structural analysis of Ab-antigen (Ag) interactions. Natural Abs, we found, recognize their Ags by combining multiple complementarity-determining regions (CDRs) to create an integrated interface. Synthetic Abs, however, rely dominantly, sometimes even exclusively on CDRH3. The increased contribution of CDRH3 to Ag binding in synthetic Abs comes with a substantial decrease in the involvement of CDRH2 and CDRH1. Furthermore, in natural Abs CDRs specialize in specific types of non-covalent interactions with the Ag. CDRH1 accounts for a significant portion of the cation-pi interactions; CDRH2 is the major source of salt-bridges and CDRH3 accounts for most hydrogen bonds. In synthetic Abs this specialization is lost, and CDRH3 becomes the main sources of all types of contacts. The reliance of synthetic Abs on CDRH3 reduces the complexity of their interaction with the Ag: More Ag residues contact only one CDR and fewer contact 3 CDRs or more. We suggest that the focus of engineering attempts on CDRH3 results in libraries enriched with variants that are not natural-like. This may affect not only Ag binding, but also Ab expression, stability and selectivity. Our findings can help guide library design, creating libraries that can bind more epitopes and Abs that better mimic the natural antigenic interactions.

Antibody specific epitope prediction-emergence of a new paradigm

The development of accurate tools for predicting B-cell epitopes is important but difficult. Traditional methods have examined which regions in an antigen are likely binding sites of an antibody. However, it is becoming increasingly clear that most antigen surface residues will be able to bind one or more of the myriad of possible antibodies. In recent years, new approaches have emerged for predicting an epitope for a specific antibody, utilizing information encoded in antibody sequence or structure. Applying such antibody-specific predictions to groups of antibodies in combination with easily obtainable experimental data improves the performance of epitope predictions. We expect that further advances of such tools will be possible with the integration of immunoglobulin repertoire sequencing data.

PEASE: predicting B-cell epitopes utilizing antibody sequence

Antibody epitope mapping is a key step in understanding antibody-antigen recognition and is of particular interest for drug development, diagnostics and vaccine design. Most computational methods for epitope prediction are based on properties of the antigen sequence and/or structure, not taking into account the antibody for which the epitope is predicted. Here, we introduce PEASE, a web server predicting antibody-specific epitopes, utilizing the sequence of the antibody. The predictions are provided both at the residue level and as patches on the antigen structure. The tradeoff between recall and precision can be tuned by the user, by changing the default parameters. The results are provided as text and HTML files as well as a graph, and can be viewed on the antigen 3D structure.

AVAILABILITY AND IMPLEMENTATION

PEASE is freely available on the web at www.ofranlab.org/PEASE.

CONTACT

yanay@ofranlab.org.

Large scale analysis of phenotype-pathway relationships based on GWAS results

The widely used pathway-based approach for interpreting Genome Wide Association Studies (GWAS), assumes that since function is executed through the interactions of multiple genes, different perturbations of the same pathway would result in a similar phenotype. This assumption, however, was not systemically assessed on a large scale. To determine whether SNPs associated with a given complex phenotype affect the same pathways more than expected by chance, we analyzed 368 phenotypes that were studied in >5000 GWAS. We found 216 significant phenotype-pathway associations between 70 of the phenotypes we analyzed and known pathways. We also report 391 strong phenotype-phenotype associations between phenotypes that are affected by the same pathways. While some of these associations confirm previously reported connections, others are new and could shed light on the molecular basis of these diseases. Our findings confirm that phenotype-associated SNPs cluster into pathways much more than expected by chance. However, this is true for <20% (70/368) of the phenotypes. Different types of phenotypes show markedly different tendencies: Virtually all autoimmune phenotypes show strong clustering of SNPs into pathways, while most cancers and metabolic conditions, and all electrophysiological phenotypes, could not be significantly associated with any pathway despite being significantly associated with a large number of SNPs. While this may be due to missing data, it may also suggest that these phenotypes could result only from perturbations of specific genes and not from other perturbations of the same pathway. Further analysis of pathway-associated versus gene-associated phenotypes is, therefore, needed in order to understand disease etiology and in order to promote better drug target selection.

Co-expression and co-localization of hub proteins and their partners are encoded in protein sequence

Spatiotemporal coordination is a critical factor in biological processes. Some hubs in protein-protein interaction networks tend to be co-expressed and co-localized with their partners more strongly than others, a difference which is arguably related to functional differences between the hubs. Based on numerous analyses of yeast hubs, it has been suggested that differences in co-expression and co-localization are reflected in the structural and molecular characteristics of the hubs. We hypothesized that if indeed differences in co-expression and co-localization are encoded in the molecular characteristics of the protein, it may be possible to predict the tendency for co-expression and co-localization of human hubs based on features learned from systematically characterized yeast hubs. Thus, we trained a prediction algorithm on hubs from yeast that were classified as either strongly or weakly co-expressed and co-localized with their partners, and applied the trained model to 800 human hub proteins. We found that the algorithm significantly distinguishes between human hubs that are co-expressed and co-localized with their partners and hubs that are not. The prediction is based on sequence derived features such as "stickiness", i.e. the existence of multiple putative binding sites that enable multiple simultaneous interactions, "plasticity", i.e. the existence of predicted structural disorder which conjecturally allows for multiple consecutive interactions with the same binding site and predicted subcellular localization. These results suggest that spatiotemporal dynamics is encoded, at least in part, in the amino acid sequence of the protein and that this encoding is similar in yeast and in human.

Large-scale analysis of somatic hypermutations in antibodies reveals which structural regions, positions and amino acids are modified to improve affinity

The principles of affinity maturation of antibodies (Abs), which underlies B cell-mediated immunity, are still under debate. It is unclear whether the antigen (Ag) binding site is a preferred target for mutations, and what the role of activation-induced deaminase (AID) hotspots is in this process. Here we report a structural analysis of 3495 residues that have been replaced through somatic hypermutations (SHMs) in 196 Abs. We show that there is no correlation between the propensity of an amino acid to be in AID hotspot and the probability that it is replaced during the SHM process. Although AID hotspots may be necessary to enable SHMs, they are not a major driving force in determining which residues are mutated. We identified Ab positions that are highly mutated and significantly affect binding. The effect of mutation on binding energy is a major factor in determining which structural regions of the Ab are mutated. There is a clear preference for mutations at the Ag-binding site. However, positions outside this region that also affect binding are often preferred targets for SHMs. As for amino acid preferences, a general trend during SHM is to make Ab-Ag interfaces more similar to protein-protein interfaces in general. In different regions of the Ab, there are different sets of preferences for amino acid substitution. This mapping improves our understanding of Ab affinity maturation and may assist in Ab engineering.

The structural basis of antibody-antigen recognition

The function of antibodies (Abs) involves specific binding to antigens (Ags) and activation of other components of the immune system to fight pathogens. The six hypervariable loops within the variable domains of Abs, commonly termed complementarity determining regions (CDRs), are widely assumed to be responsible for Ag recognition, while the constant domains are believed to mediate effector activation. Recent studies and analyses of the growing number of available Ab structures, indicate that this clear functional separation between the two regions may be an oversimplification. Some positions within the CDRs have been shown to never participate in Ag binding and some off-CDRs residues often contribute critically to the interaction with the Ag. Moreover, there is now growing evidence for non-local and even allosteric effects in Ab-Ag interaction in which Ag binding affects the constant region and vice versa. This review summarizes and discusses the structural basis of Ag recognition, elaborating on the contribution of different structural determinants of the Ab to Ag binding and recognition. We discuss the CDRs, the different approaches for their identification and their relationship to the Ag interface. We also review what is currently known about the contribution of non-CDRs regions to Ag recognition, namely the framework regions (FRs) and the constant domains. The suggested mechanisms by which these regions contribute to Ag binding are discussed. On the Ag side of the interaction, we discuss attempts to predict B-cell epitopes and the suggested idea to incorporate Ab information into B-cell epitope prediction schemes. Beyond improving the understanding of immunity, characterization of the functional role of different parts of the Ab molecule may help in Ab engineering, design of CDR-derived peptides, and epitope prediction.

Assessing the relationship between conservation of function and conservation of sequence using photosynthetic proteins

MOTIVATION

Assessing the false positive rate of function prediction methods is difficult, as it is hard to establish that a protein does not have a certain function. To determine to what extent proteins with similar sequences have a common function, we focused on photosynthesis-related proteins. A protein that comes from a non-photosynthetic organism is, undoubtedly, not involved in photosynthesis.

RESULTS

We show that function diverges very rapidly: 70% of the close homologs of photosynthetic proteins come from non-photosynthetic organisms. Therefore, high sequence similarity, in most cases, is not tantamount to similar function. However, we found that many functionally similar proteins often share short sequence elements, which may correspond to a functional site and could reveal functional similarities more accurately than sequence similarity.

CONCLUSIONS

These results shed light on the way biological function is conserved in evolution and may help improve large-scale analysis of protein function.

A systematic comparison of free and bound antibodies reveals binding-related conformational changes

To study structural changes that occur in Abs upon Ag binding, we systematically compared free and bound structures of all 141 crystal structures of the 49 Abs that were solved in these two forms. We found that many structural changes occur far from the Ag binding site. Some of them may constitute a mechanism for the recently suggested allosteric effects in Abs. Within the binding site itself, CDR-H3 is the only element that shows significant binding-related conformational changes; however, this occurs in only one third of the Abs. Beyond the binding site, Ag binding is associated with changes in the relative orientation of the H and L chains in both the variable and constant domains. An even larger change occurs in the elbow angle between the variable and the constant domains, and it is significantly larger for binding of big Ags than for binding of small ones. The most consistent and substantial conformational changes occur in a loop in the H chain constant domain. This loop is implicated in the interaction between the H and L chains, is often intrinsically disordered, and is involved in complement binding. Hence, we suggest that it may have a role in Ab function. These findings provide structural insight into the recently proposed allosteric effects in Abs.

Static network structure can be used to model the phenotypic effects of perturbations in regulatory networks

MOTIVATION

Biological processes are dynamic, whereas the networks that depict them are typically static. Quantitative modeling using differential equations or logic-based functions can offer quantitative predictions of the behavior of biological systems, but they require detailed experimental characterization of interaction kinetics, which is typically unavailable. To determine to what extent complex biological processes can be modeled and analyzed using only the static structure of the network (i.e. the direction and sign of the edges), we attempt to predict the phenotypic effect of perturbations in biological networks from the static network structure.

RESULTS

We analyzed three networks from different sources: The EGFR/MAPK and PI3K/AKT network from a detailed experimental study, the TNF regulatory network from the STRING database and a large network of all NCI-curated pathways from the Protein Interaction Database. Altogether, we predicted the effect of 39 perturbations (e.g. by one or two drugs) on 433 target proteins/genes. In up to 82% of the cases, an algorithm that used only the static structure of the network correctly predicted whether any given protein/gene is upregulated or downregulated as a result of perturbations of other proteins/genes.

CONCLUSION

While quantitative modeling requires detailed experimental data and heavy computations, which limit its scalability for large networks, a wiring-based approach can use available data from pathway and interaction databases and may be scalable. These results lay the foundations for a large-scale approach of predicting phenotypes based on the schematic structure of networks.

Paratome: an online tool for systematic identification of antigen-binding regions in antibodies based on sequence or structure

Antibodies are capable of specifically recognizing and binding antigens. Identification of the antigen-binding site, commonly dubbed paratope, is of high importance both for medical and biological applications. To date, the identification of antigen-binding regions (ABRs) relies on tools for the identification of complementarity-determining regions (CDRs). However, we have shown that up to 22% of the residues that actually bind the antigen fall outside the traditionally defined CDRs. The Paratome web server predicts the ABRs of an antibody, given its amino acid sequence or 3D structure. It is based on a set of consensus regions derived from a structural alignment of a non-redundant set of all known antibody-antigen complexes. Given a query sequence or structure, the server identifies the regions in the query antibody that correspond to the consensus ABRs. An independent set of antibody-antigen complexes was used to test the server and it was shown to correctly identify at least 94% of the antigen-binding residues. The Paratome web server is freely available at http://www.ofranlab.org/paratome/.

Structural consensus among antibodies defines the antigen binding site

The Complementarity Determining Regions (CDRs) of antibodies are assumed to account for the antigen recognition and binding and thus to contain also the antigen binding site. CDRs are typically discerned by searching for regions that are most different, in sequence or in structure, between different antibodies. Here, we show that ∼20% of the antibody residues that actually bind the antigen fall outside the CDRs. However, virtually all antigen binding residues lie in regions of structural consensus across antibodies. Furthermore, we show that these regions of structural consensus which cover the antigen binding site are identifiable from the sequence of the antibody. Analyzing the predicted contribution of antigen binding residues to the stability of the antibody-antigen complex, we show that residues that fall outside of the traditionally defined CDRs are at least as important to antigen binding as residues within the CDRs, and in some cases, they are even more important energetically. Furthermore, antigen binding residues that fall outside of the structural consensus regions but within traditionally defined CDRs show a marginal energetic contribution to antigen binding. These findings allow for systematic and comprehensive identification of antigen binding sites, which can improve the understanding of antigenic interactions and may be useful in antibody engineering and B-cell epitope identification.

Antibodies are a primary adaptive defence mechanism against infection, and function by recognizing and binding to non-self antigens. While most of the sequence of all antibodies of a given individual is identical, relatively small variations turn each antibody into a specific binder of one antigen. It is widely assumed that antigen binding sites correspond to the so called Complementarity Determining Regions (CDRs) of the antibody, which are defined as the elements that are most different between antibodies. We analysed all known antibody-antigen complexes and found that about 20% of the residues that actually bind the antigen fall outside the CDRs. However, we also found that virtually all antigen binding residues fall within regions of structural consensus between antibodies. Moreover, we demonstrate that antigen binding residues that reside within these structural consensus regions but outside of the traditionally-defined CDRs make significant energetic contribution to antigen binding. Furthermore, we show that these regions are organized along the sequence of the antibody chains and are identifiable from the sequence of the antibody.

Unveiling protein functions through the dynamics of the interaction network

Protein interaction networks have become a tool to study biological processes, either for predicting molecular functions or for designing proper new drugs to regulate the main biological interactions. Furthermore, such networks are known to be organized in sub-networks of proteins contributing to the same cellular function. However, the protein function prediction is not accurate and each protein has traditionally been assigned to only one function by the network formalism. By considering the network of the physical interactions between proteins of the yeast together with a manual and single functional classification scheme, we introduce a method able to reveal important information on protein function, at both micro- and macro-scale. In particular, the inspection of the properties of oscillatory dynamics on top of the protein interaction network leads to the identification of misclassification problems in protein function assignments, as well as to unveil correct identification of protein functions. We also demonstrate that our approach can give a network representation of the meta-organization of biological processes by unraveling the interactions between different functional classes.

Post-streptococcal auto-antibodies inhibit protein disulfide isomerase and are associated with insulin resistance

Post-streptococcal autoimmunity affects millions worldwide, targeting multiple organs including the heart, brain, and kidneys. To explore the post-streptococcal autoimmunity spectrum, we used western blot analyses, to screen 310 sera from healthy subjects with (33%) and without (67%) markers of recent streptococcal infections [anti-Streptolysin O (ASLO) or anti-DNAse B (ADB)]. A 58 KDa protein, reacting strongly with post-streptococcal sera, was identified as Protein Disulfide Isomerase (PDI), an abundant protein with pleiotropic metabolic, immunologic, and thrombotic effects. Anti-PDI autoantibodies, purified from human sera, targeted similar epitopes in Streptolysin O (SLO, P51-61) and PDI (P328-338). The correlation between post-streptococcal status and anti-human PDI auto-immunity was further confirmed in a total of 2987 samples (13.6% in 530 ASLO positive versus 5.6% in 2457 ASLO negative samples, p<0.0001). Finally, anti-PDI auto-antibodies inhibited PDI-mediated insulin degradation in vitro (n = 90, p<0.001), and correlated with higher serum insulin (14.1 iu/ml vs. 12.2 iu/ml, n = 1215, p = 0.039) and insulin resistance (Homeostatic Model Assessment (HOMA) 4.1 vs. 3.1, n = 1215, p = 0.004), in a population-based cohort. These results identify PDI as a major target of post-streptococcal autoimmunity, and establish a new link between infection, autoimmunity, and metabolic disturbances.

Large-scale analysis of secondary structure changes in proteins suggests a role for disorder-to-order transitions in nucleotide binding proteins

Conformational changes in proteins often involve secondary structure transitions. Such transitions can be divided into two types: disorder-to-order changes, in which a disordered segment acquires an ordered secondary structure (e.g., disorder to alpha-helix, disorder to beta-strand), and order-to-order changes, where a segment switches from one ordered secondary structure to another (e.g., alpha-helix to beta-strand, alpha-helix to turn). In this study, we explore the distribution of these transitions in the proteome. Using a comprehensive, yet highly conservative method, we compared solved three-dimensional structures of identical protein sequences, looking for differences in the secondary structures with which they were assigned. Protein chains in which such secondary structure transitions were detected, were classified into two sets according to the type of transition that is involved (disorder-to-order or order-to-order), allowing us to characterize each set by examining enrichment of gene ontology terms. The results reveal that the disorder-to-order set is significantly enriched with nucleotide binding proteins, whereas the order-to-order set is more diverse. Remarkably, further examination reveals that >22% of the purine nucleotide binding proteins include segments which undergo disorder-to-order transitions, suggesting that such transitions play an important role in this process.

New in protein structure and function annotation: hotspots, single nucleotide polymorphisms and the 'Deep Web'

The rapidly increasing quantity of protein sequence data continues to widen the gap between available sequences and annotations. Comparative modeling suggests some aspects of the 3D structures of approximately half of all known proteins; homology- and network-based inferences annotate some aspect of function for a similar fraction of the proteome. For most known protein sequences, however, there is detailed knowledge about neither their function nor their structure. Comprehensive efforts towards the expert curation of sequence annotations have failed to meet the demand of the rapidly increasing number of available sequences. Only the automated prediction of protein function in the absence of homology can close the gap between available sequences and annotations in the foreseeable future. This review focuses on two novel methods for automated annotation, and briefly presents an outlook on how modern web software may revolutionize the field of protein sequence annotation. First, predictions of protein binding sites and functional hotspots, and the evolution of these into the most successful type of prediction of protein function from sequence will be discussed. Second, a new tool, comprehensive in silico mutagenesis, which contributes important novel predictions of function and at the same time prepares for the onset of the next sequencing revolution, will be described. While these two new sub-fields of protein prediction represent the breakthroughs that have been achieved methodologically, it will then be argued that a different development might further change the way biomedical researchers benefit from annotations: modern web software can connect the worldwide web in any browser with the 'Deep Web' (ie, proprietary data resources). The availability of this direct connection, and the resulting access to a wealth of data, may impact drug discovery and development more than any existing method that contributes to protein annotation.

Granulocyte transfusions for neutropenic patients with life-threatening infections: a single centre experience in 47 patients, who received 348 granulocyte transfusions

INTRODUCTION

The role of granulocyte transfusions (GT) in patients with neutropenia-related infections remains controversial.

MATERIALS AND METHODS

A retrospective analysis of 47 neutropenic patients, treated with 348 consecutive GTs for life-threatening infections between 1999 and 2004, is presented.

RESULTS

The only grade III-IV toxicity observed in GT recipients was respiratory deterioration (n = 6, 12.8%). The overall infection-related mortality (IRM) approached 38%. Achievement of a neutrophil count of > 700 cells per microl after at least 50% of days of GTs (n = 33, 70%) significantly correlated with reduced IRM (27.3% vs. 64.3%, P < 0.02). GT doses of > 2 x 10(10) neutrophils per bag appeared to increase both neutophil and platelet counts following transfusion.

CONCLUSION

GTs are safe and should be considered for patients with life-threatening neutropenic infections. However, prospective randomized studies of GTs are the only way to establish the true role of GTs.

Prediction of DNA-binding residues from sequence

MOTIVATION

Thousands of proteins are known to bind to DNA; for most of them the mechanism of action and the residues that bind to DNA, i.e. the binding sites, are yet unknown. Experimental identification of binding sites requires expensive and laborious methods such as mutagenesis and binding essays. Hence, such studies are not applicable on a large scale. If the 3D structure of a protein is known, it is often possible to predict DNA-binding sites in silico. However, for most proteins, such knowledge is not available.

RESULTS

It has been shown that DNA-binding residues have distinct biophysical characteristics. Here we demonstrate that these characteristics are so distinct that they enable accurate prediction of the residues that bind DNA directly from amino acid sequence, without requiring any additional experimental or structural information. In a cross-validation based on the largest non-redundant dataset of high-resolution protein-DNA complexes available today, we found that 89% of our predictions are confirmed by experimental data. Thus, it is now possible to identify DNA-binding sites on a proteomic scale even in the absence of any experimental data or 3D-structural information.

AVAILABILITY

http://cubic.bioc.columbia.edu/services/disis.

Towards a consensus on datasets and evaluation metrics for developing B-cell epitope prediction tools

A B-cell epitope is the three-dimensional structure within an antigen that can be bound to the variable region of an antibody. The prediction of B-cell epitopes is highly desirable for various immunological applications, but has presented a set of unique challenges to the bioinformatics and immunology communities. Improving the accuracy of B-cell epitope prediction methods depends on a community consensus on the data and metrics utilized to develop and evaluate such tools. A workshop, sponsored by the National Institute of Allergy and Infectious Disease (NIAID), was recently held in Washington, DC to discuss the current state of the B-cell epitope prediction field. Many of the currently available tools were surveyed and a set of recommendations was devised to facilitate improvements in the currently existing tools and to expedite future tool development. An underlying theme of the recommendations put forth by the panel is increased collaboration among research groups. By developing common datasets, standardized data formats, and the means with which to consolidate information, we hope to greatly enhance the development of B-cell epitope prediction tools.

Protein-protein interaction hotspots carved into sequences

Protein-protein interactions, a key to almost any biological process, are mediated by molecular mechanisms that are not entirely clear. The study of these mechanisms often focuses on all residues at protein-protein interfaces. However, only a small subset of all interface residues is actually essential for recognition or binding. Commonly referred to as "hotspots," these essential residues are defined as residues that impede protein-protein interactions if mutated. While no in silico tool identifies hotspots in unbound chains, numerous prediction methods were designed to identify all the residues in a protein that are likely to be a part of protein-protein interfaces. These methods typically identify successfully only a small fraction of all interface residues. Here, we analyzed the hypothesis that the two subsets correspond (i.e., that in silico methods may predict few residues because they preferentially predict hotspots). We demonstrate that this is indeed the case and that we can therefore predict directly from the sequence of a single protein which residues are interaction hotspots (without knowledge of the interaction partner). Our results suggested that most protein complexes are stabilized by similar basic principles. The ability to accurately and efficiently identify hotspots from sequence enables the annotation and analysis of protein-protein interaction hotspots in entire organisms and thus may benefit function prediction and drug development. The server for prediction is available at http://www.rostlab.org/services/isis.

ISIS: interaction sites identified from sequence

MOTIVATION

Large-scale experiments reveal pairs of interacting proteins but leave the residues involved in the interactions unknown. These interface residues are essential for understanding the mechanism of interaction and are often desired drug targets. Reliable identification of residues that reside in protein-protein interface typically requires analysis of protein structure. Therefore, for the vast majority of proteins, for which there is no high-resolution structure, there is no effective way of identifying interface residues.

RESULTS

Here we present a machine learning-based method that identifies interacting residues from sequence alone. Although the method is developed using transient protein-protein interfaces from complexes of experimentally known 3D structures, it never explicitly uses 3D information. Instead, we combine predicted structural features with evolutionary information. The strongest predictions of the method reached over 90% accuracy in a cross-validation experiment. Our results suggest that despite the significant diversity in the nature of protein-protein interactions, they all share common basic principles and that these principles are identifiable from sequence alone.

Create and assess protein networks through molecular characteristics of individual proteins

MOTIVATION

The study of biological systems, pathways and processes relies increasingly on analyses of networks. Most often, such analyses focus on network topology, thereby treating all proteins or genes as identical, featureless nodes. Integrating molecular data and insights about the qualities of individual proteins into the analysis may enhance our ability to decipher biological pathways and processes.

RESULTS

Here, we introduce a novel platform for data integration that generates networks on the macro system-level, analyzes the molecular characteristics of each protein on the micro level, and then combines the two levels by using the molecular characteristics to assess networks. It also annotates the function and subcellular localization of each protein and displays the process on an image of a cell, rendering each protein in its respective cellular compartment. By thus visualizing the network in a cellular context we are able to analyze pathways and processes in a novel way. As an example, we use the system to analyze proteins implicated with Alzheimers disease and show how the integrated view corroborates previous observations and how it helps in the formulation of new hypotheses regarding the molecular underpinnings of the disease.

AVAILABILITY

http://www.rostlab.org/services/pinat.

Proteins of the same fold and unrelated sequences have similar amino acid composition

It is well established that there is a relationship between the amino acid composition of a protein and its structural class (i.e., alpha, beta, alpha + beta, or alpha/beta). Several studies have even shown the power of amino acid composition in predicting the secondary structure class of a protein. Herein, we show that significant similarity in amino acid composition exists not only between proteins of the same class, but even between proteins of the same fold. To test conjectural explanations for this phenomenon, we analyzed a set of structurally similar proteins that are dissimilar in sequence. Based on this analysis, we suggest that specific residues that are involved in intramolecular interactions may account for this surprising relationship between composition and structure.

Beyond annotation transfer by homology: novel protein-function prediction methods to assist drug discovery

Every entirely sequenced genome reveals 100 s to 1000 s of protein sequences for which the only annotation available is 'hypothetical protein'. Thus, in the human genome and in the genomes of pathogenic agents there could be 1000 s of potential, unexplored drug targets. Computational prediction of protein function can play a role in studying these targets. We shall review the challenges, research approaches and recently developed tools in the field of computational function-prediction and we will discuss the ways these issues can change the process of drug discovery.