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.

Exploring Co-occurring POLE Exonuclease and Non-exonuclease Domain Mutations and Their Impact on Tumor Mutagenicity

POLE driver mutations in the exonuclease domain (ExoD driver) are prevalent in several cancers, including colorectal cancer and endometrial cancer, leading to dramatically ultra-high tumor mutation burden (TMB). To understand whether POLE mutations that are not classified as drivers (POLE Variant) contribute to mutagenesis, we assessed TMB in 447 POLE-mutated colorectal cancers, endometrial cancers, and ovarian cancers classified as TMB-high ≥10 mutations/Mb (mut/Mb) or TMB-low <10 mut/Mb. TMB was significantly highest in tumors with "POLE ExoD driver plus POLE Variant" (colorectal cancer and endometrial cancer, P < 0.001; ovarian cancer, P < 0.05). TMB increased with additional POLE variants (P < 0.001), but plateaued at 2, suggesting an association between the presence of these variants and TMB. Integrated analysis of AlphaFold2 POLE models and quantitative stability estimates predicted the impact of multiple POLE variants on POLE functionality. The prevalence of immunogenic neoepitopes was notably higher in the "POLE ExoD driver plus POLE Variant" tumors. Overall, this study reveals a novel correlation between POLE variants in POLE ExoD-driven tumors, and ultra-high TMB. Currently, only select pathogenic ExoD mutations with a reliable association with ultra-high TMB inform clinical practice. Thus, these findings are hypothesis-generating, require functional validation, and could potentially inform tumor classification, treatment responses, and clinical outcomes.

SIGNIFICANCE

Somatic POLE ExoD driver mutations cause proofreading deficiency that induces high TMB. This study suggests a novel modifier role for POLE variants in POLE ExoD-driven tumors, associated with ultra-high TMB. These data, in addition to future functional studies, may inform tumor classification, therapeutic response, and patient outcomes.

AlphaFold2 models of the active form of all 437 catalytically competent human protein kinase domains

Humans have 437 catalytically competent protein kinase domains with the typical kinase fold, similar to the structure of Protein Kinase A (PKA). Only 155 of these kinases are in the Protein Data Bank in their active form. The active form of a kinase must satisfy requirements for binding ATP, magnesium, and substrate. From structural bioinformatics analysis of 40 unique substrate-bound kinases, we derived several criteria for the active form of protein kinases. We include requirements on the DFG motif of the activation loop but also on the positions of the N-terminal and C-terminal segments of the activation loop that must be placed appropriately to bind substrate. Because the active form of catalytic kinases is needed for understanding substrate specificity and the effects of mutations on catalytic activity in cancer and other diseases, we used AlphaFold2 to produce models of all 437 human protein kinases in the active form. This was accomplished with templates in the active form from the PDB and shallow multiple sequence alignments of orthologs and close homologs of the query protein. We selected models for each kinase based on the pLDDT scores of the activation loop residues, demonstrating that the highest scoring models have the lowest or close to the lowest RMSD to 22 non-redundant substrate-bound structures in the PDB. A larger benchmark of all 130 active kinase structures with complete activation loops in the PDB shows that 80% of the highest-scoring AlphaFold2 models have RMSD < 1.0 Å and 90% have RMSD < 2.0 Å over the activation loop backbone atoms. Models for all 437 catalytic kinases are available at http://dunbrack.fccc.edu/kincore/activemodels. We believe they may be useful for interpreting mutations leading to constitutive catalytic activity in cancer as well as for templates for modeling substrate and inhibitor binding for molecules which bind to the active state.

Palmitoylation-dependent control of JAK1 kinase signaling governs responses to neuropoietic cytokines and survival in DRG neurons

Janus Kinase-1 (JAK1) plays key roles during neurodevelopment and following neuronal injury, while activatory JAK1 mutations are linked to leukemia. In mice, Jak1 genetic deletion results in perinatal lethality, suggesting non-redundant roles and/or regulation of JAK1 for which other JAKs cannot compensate. Proteomic studies reveal that JAK1 is more likely palmitoylated compared to other JAKs, implicating palmitoylation as a possible JAK1-specific regulatory mechanism. However, the importance of palmitoylation for JAK1 signaling has not been addressed. Here, we report that JAK1 is palmitoylated in transfected HEK293T cells and endogenously in cultured Dorsal Root Ganglion (DRG) neurons. We further use comprehensive screening in transfected non-neuronal cells and shRNA-mediated knockdown in DRG neurons to identify the related enzymes ZDHHC3 and ZDHHC7 as dominant protein acyltransferases (PATs) for JAK1. Surprisingly, we found palmitoylation minimally affects JAK1 localization in neurons, but is critical for JAK1's kinase activity in cells and even in vitro. We propose this requirement is likely because palmitoylation facilitates transphosphorylation of key sites in JAK1's activation loop, a possibility consistent with structural models of JAK1. Importantly, we demonstrate a leukemia-associated JAK1 mutation overrides the palmitoylation-dependence of JAK1 activity, potentially explaining why this mutation is oncogenic. Finally, we show that JAK1 palmitoylation is important for neuropoietic cytokine-dependent signaling and neuronal survival and that combined Zdhhc3/7 loss phenocopies loss of palmitoyl-JAK1. These findings provide new insights into the control of JAK signaling in both physiological and pathological contexts.

Corrigendum to "Computational design of nanomolar-binding antibodies specific to multiple SARS-CoV-2 variants by engineering a specificity switch of antibody 80R using RosettaAntibodyDesign (RAbD) results in potential generalizable therapeutic antibodies for novel SARS-CoV-2 virus" [Heliyon 9(4) (April 2023) e15032]

[This corrects the article DOI: 10.1016/j.heliyon.2023.e15032.].

Candidate variants in DNA replication and repair genes in early-onset renal cell carcinoma patients referred for germline testing

BACKGROUND

Early-onset renal cell carcinoma (eoRCC) is typically associated with pathogenic germline variants (PGVs) in RCC familial syndrome genes. However, most eoRCC patients lack PGVs in familial RCC genes and their genetic risk remains undefined.

METHODS

Here, we analyzed biospecimens from 22 eoRCC patients that were seen at our institution for genetic counseling and tested negative for PGVs in RCC familial syndrome genes.

RESULTS

Analysis of whole-exome sequencing (WES) data found enrichment of candidate pathogenic germline variants in DNA repair and replication genes, including multiple DNA polymerases. Induction of DNA damage in peripheral blood monocytes (PBMCs) significantly elevated numbers of [Formula: see text]H2AX foci, a marker of double-stranded breaks, in PBMCs from eoRCC patients versus PBMCs from matched cancer-free controls. Knockdown of candidate variant genes in Caki RCC cells increased [Formula: see text]H2AX foci. Immortalized patient-derived B cell lines bearing the candidate variants in DNA polymerase genes (POLD1, POLH, POLE, POLK) had DNA replication defects compared to control cells. Renal tumors carrying these DNA polymerase variants were microsatellite stable but had a high mutational burden. Direct biochemical analysis of the variant Pol δ and Pol η polymerases revealed defective enzymatic activities.

CONCLUSIONS

Together, these results suggest that constitutional defects in DNA repair underlie a subset of eoRCC cases. Screening patient lymphocytes to identify these defects may provide insight into mechanisms of carcinogenesis in a subset of genetically undefined eoRCCs. Evaluation of DNA repair defects may also provide insight into the cancer initiation mechanisms for subsets of eoRCCs and lay the foundation for targeting DNA repair vulnerabilities in eoRCC.

Computational design of nanomolar-binding antibodies specific to multiple SARS-CoV-2 variants by engineering a specificity switch of antibody 80R using RosettaAntibodyDesign (RAbD) results in potential generalizable therapeutic antibodies for novel SARS-CoV-2 virus

The human infectious disease COVID-19 caused by the SARS-CoV-2 virus has become a major threat to global public health. Developing a vaccine is the preferred prophylactic response to epidemics and pandemics. However, for individuals who have contracted the disease, the rapid design of antibodies that can target the SARS-CoV-2 virus fulfils a critical need. Further, discovering antibodies that bind multiple variants of SARS-CoV-2 can aid in the development of rapid antigen tests (RATs) which are critical for the identification and isolation of individuals currently carrying COVID-19. Here we provide a proof-of-concept study for the computational design of high-affinity antibodies that bind to multiple variants of the SARS-CoV-2 spike protein using RosettaAntibodyDesign (RAbD). Well characterized antibodies that bind with high affinity to the SARS-CoV-1 (but not SARS-CoV-2) spike protein were used as templates and re-designed to bind the SARS-CoV-2 spike protein with high affinity, resulting in a specificity switch. A panel of designed antibodies were experimentally validated. One design bound to a broad range of variants of concern including the Omicron, Delta, Wuhan, and South African spike protein variants.

FAM122A ensures cell cycle interphase progression and checkpoint control as a SLiM-dependent substrate-competitive inhibitor to the B55⍺/PP2A phosphatase

The Ser/Thr protein phosphatase 2A (PP2A) is a highly conserved collection of heterotrimeric holoenzymes responsible for the dephosphorylation of many regulated phosphoproteins. Substrate recognition and the integration of regulatory cues are mediated by B regulatory subunits that are complexed to the catalytic subunit (C) by a scaffold protein (A). PP2A/B55 substrate recruitment was thought to be mediated by charge-charge interactions between the surface of B55α and its substrates. Challenging this view, we recently discovered a conserved SLiM [ RK ]- V -x-x-[ VI ]- R in a range of proteins, including substrates such as the retinoblastoma-related protein p107 and TAU (Fowle et al. eLife 2021;10:e63181). Here we report the identification of this SLiM in FAM122A, an inhibitor of B55α/PP2A. This conserved SLiM is necessary for FAM122A binding to B55α in vitro and in cells. Computational structure prediction with AlphaFold2 predicts an interaction consistent with the mutational and biochemical data and supports a mechanism whereby FAM122A uses the 'SLiM' in the form of a short α-helix to dock to the B55α top groove. In this model, FAM122A spatially constrains substrate access by occluding the catalytic subunit with a second α-helix immediately adjacent to helix 1. Consistently, FAM122A functions as a competitive inhibitor as it prevents binding of substrates in in vitro competition assays and the dephosphorylation of CDK substrates by B55α/PP2A in cell lysates. Ablation of FAM122A in human cell lines reduces the rate of proliferation, progression through cell cycle transitions and abrogates G1/S and intra-S phase cell cycle checkpoints. FAM122A-KO in HEK293 cells results in attenuation of CHK1 and CHK2 activation in response to replication stress. Overall, these data strongly suggest that FAM122A is a 'SLiM'-dependent, substrate-competitive inhibitor of B55α/PP2A that suppresses multiple functions of B55α in the DNA damage response and in timely progression through the cell cycle interphase.

Multiplexed screens identify RAS paralogues HRAS and NRAS as suppressors of KRAS-driven lung cancer growth

Oncogenic KRAS mutations occur in approximately 30% of lung adenocarcinoma. Despite several decades of effort, oncogenic KRAS-driven lung cancer remains difficult to treat, and our understanding of the regulators of RAS signalling is incomplete. Here to uncover the impact of diverse KRAS-interacting proteins on lung cancer growth, we combined multiplexed somatic CRISPR/Cas9-based genome editing in genetically engineered mouse models with tumour barcoding and high-throughput barcode sequencing. Through a series of CRISPR/Cas9 screens in autochthonous lung cancer models, we show that HRAS and NRAS are suppressors of KRASG12D-driven tumour growth in vivo and confirm these effects in oncogenic KRAS-driven human lung cancer cell lines. Mechanistically, RAS paralogues interact with oncogenic KRAS, suppress KRAS-KRAS interactions, and reduce downstream ERK signalling. Furthermore, HRAS and NRAS mutations identified in oncogenic KRAS-driven human tumours partially abolished this effect. By comparing the tumour-suppressive effects of HRAS and NRAS in oncogenic KRAS- and oncogenic BRAF-driven lung cancer models, we confirm that RAS paralogues are specific suppressors of KRAS-driven lung cancer in vivo. Our study outlines a technological avenue to uncover positive and negative regulators of oncogenic KRAS-driven cancer in a multiplexed manner in vivo and highlights the role RAS paralogue imbalance in oncogenic KRAS-driven lung cancer.

Delineating The RAS Conformational Landscape

Mutations in RAS isoforms (KRAS, NRAS, and HRAS) are among the most frequent oncogenic alterations in many cancers, making these proteins high priority therapeutic targets. Effectively targeting RAS isoforms requires an exact understanding of their active, inactive, and druggable conformations. However, there is no structural catalog of RAS conformations to guide therapeutic targeting or examining the structural impact of RAS mutations. Here we present an expanded classification of RAS conformations based on analyses of the catalytic switch 1 (SW1) and switch 2 (SW2) loops. From 721 human KRAS, NRAS, and HRAS structures available in the Protein Data Bank (206 RAS-protein co-complexes, 190 inhibitor-bound, and 325 unbound, including 204 WT and 517 mutated structures), we created a broad conformational classification based on the spatial positions of Y32 in SW1 and Y71 in SW2. Clustering all well-modeled SW1 and SW2 loops using a density-based machine learning algorithm defined additional conformational subsets, some previously undescribed. Three SW1 conformations and nine SW2 conformations were identified, each associated with different nucleotide states (GTP-bound, nucleotide-free, and GDP-bound) and specific bound proteins or inhibitor sites. The GTP-bound SW1 conformation could be further subdivided based on the hydrogen bond type made between Y32 and the GTP γ-phosphate. Further analysis clarified the catalytic impact of G12D and G12V mutations and the inhibitor chemistries that bind to each druggable RAS conformation. Overall, this study has expanded our understanding of RAS structural biology, which could facilitate future RAS drug discovery.

Kincore: a web resource for structural classification of protein kinases and their inhibitors

The active form of kinases is shared across different family members, as are several commonly observed inactive forms. We previously performed a clustering of the conformation of the activation loop of all protein kinase structures in the Protein Data Bank (PDB) into eight classes based on the dihedral angles that place the Phe side chain of the DFG motif at the N-terminus of the activation loop. Our clusters are strongly associated with the placement of the activation loop, the C-helix, and other structural elements of kinases. We present Kincore, a web resource providing access to our conformational assignments for kinase structures in the PDB. While other available databases provide conformational states or drug type but not both, KinCore includes the conformational state and the inhibitor type (Type 1, 1.5, 2, 3, allosteric) for each kinase chain. The user can query and browse the database using these attributes or determine the conformational labels of a kinase structure using the web server or a standalone program. The database and labeled structure files can be downloaded from the server. Kincore will help in understanding the conformational dynamics of these proteins and guide development of inhibitors targeting specific states. Kincore is available at http://dunbrack.fccc.edu/kincore.

PP2A/B55α substrate recruitment as defined by the retinoblastoma-related protein p107

Protein phosphorylation is a reversible post-translation modification essential in cell signaling. This study addresses a long-standing question as to how the most abundant serine/threonine protein phosphatase 2 (PP2A) holoenzyme, PP2A/B55α, specifically recognizes substrates and presents them to the enzyme active site. Here, we show how the PP2A regulatory subunit B55α recruits p107, a pRB-related tumor suppressor and B55α substrate. Using molecular and cellular approaches, we identified a conserved region 1 (R1, residues 615–626) encompassing the strongest p107 binding site. This enabled us to identify an ‘HxRVxxV_619-625’ short linear motif (SLiM) in p107 as necessary for B55α binding and dephosphorylation of the proximal pSer-615 in vitro and in cells. Numerous B55α/PP2A substrates, including TAU, contain a related SLiM C-terminal from a proximal phosphosite, ‘p[ST]-P-x(4,10)-[RK]-V-x-x-[VI]-R.’ Mutation of conserved SLiM residues in TAU dramatically inhibits dephosphorylation by PP2A/B55α, validating its generality. A data-guided computational model details the interaction of residues from the conserved p107 SLiM, the B55α groove, and phosphosite presentation. Altogether, these data provide key insights into PP2A/B55α’s mechanisms of substrate recruitment and active site engagement, and also facilitate identification and validation of new substrates, a key step towards understanding PP2A/B55α’s role in multiple cellular processes.

Hiding in plain sight: structure and sequence analysis reveals the importance of the antibody DE loop for antibody-antigen binding

Antibody variable domains contain “complementarity-determining regions” (CDRs), the loops that form the antigen binding site. CDRs1-3 are recognized as the canonical CDRs. However, a fourth loop sits adjacent to CDR1 and CDR2 and joins the D and E strands on the antibody v-type fold. This “DE loop” is usually treated as a framework region, even though mutations in the loop affect the conformation of the CDRs and residues in the DE loop occasionally contact antigen. We analyzed the length, structure, and sequence features of all DE loops in the Protein Data Bank (PDB), as well as millions of sequences from HIV-1 infected and naïve patients. We refer to the DE loop as H4 and L4 in the heavy and light chains, respectively. Clustering the backbone conformations of the most common length of L4 (6 residues) reveals four conformations: two κ-only clusters, one λ-only cluster, and one mixed κ/λ cluster. Most H4 loops are length-8 and exist primarily in one conformation; a secondary conformation represents a small fraction of H4-8 structures. H4 sequence variability exceeds that of the antibody framework in naïve human high-throughput sequences, and both L4 and H4 sequence variability from λ and heavy germline sequences exceed that of germline framework regions. Finally, we identified dozens of structures in the PDB with insertions in the DE loop, all related to broadly neutralizing HIV-1 antibodies (bNabs), as well as antibody sequences from high-throughput sequencing studies of HIV-infected individuals, illuminating a possible role in humoral immunity to HIV-1.

Structure of TFIIK for phosphorylation of CTD of RNA polymerase II

During transcription initiation, the general transcription factor TFIIH marks RNA polymerase II by phosphorylating Ser5 of the carboxyl-terminal domain (CTD) of Rpb1, which is followed by extensive modifications coupled to transcription elongation, mRNA processing, and histone dynamics. We have determined a 3.5-Å resolution cryo-electron microscopy (cryo-EM) structure of the TFIIH kinase module (TFIIK in yeast), which is composed of Kin28, Ccl1, and Tfb3, yeast homologs of CDK7, cyclin H, and MAT1, respectively. The carboxyl-terminal region of Tfb3 was lying at the edge of catalytic cleft of Kin28, where a conserved Tfb3 helix served to stabilize the activation loop in its active conformation. By combining the structure of TFIIK with the previous cryo-EM structure of the preinitiation complex, we extend the previously proposed model of the CTD path to the active site of TFIIK.

PyRosetta Jupyter Notebooks Teach Biomolecular Structure Prediction and Design

Biomolecular structure drives function, and computational capabilities have progressed such that the prediction and computational design of biomolecular structures is increasingly feasible. Because computational biophysics attracts students from many different backgrounds and with different levels of resources, teaching the subject can be challenging. One strategy to teach diverse learners is with interactive multimedia material that promotes self-paced, active learning. We have created a hands-on education strategy with a set of sixteen modules that teach topics in biomolecular structure and design, from fundamentals of conformational sampling and energy evaluation to applications like protein docking, antibody design, and RNA structure prediction. Our modules are based on PyRosetta, a Python library that encapsulates all computational modules and methods in the Rosetta software package. The workshop-style modules are implemented as Jupyter Notebooks that can be executed in the Google Colaboratory, allowing learners access with just a web browser. The digital format of Jupyter Notebooks allows us to embed images, molecular visualization movies, and interactive coding exercises. This multimodal approach may better reach students from different disciplines and experience levels as well as attract more researchers from smaller labs and cognate backgrounds to leverage PyRosetta in their science and engineering research. All materials are freely available at https://github.com/RosettaCommons/PyRosetta.notebooks.

Abstract A24: Cell adhesion molecule (CAM)-related downregulated by oncogenes (CDON) promotes ovarian cancer adhesion and survival

Ovarian carcinoma (OC) broadly describes epithelial tumors involving the ovary and represents the most lethal gynecologic malignancy. The most frequently diagnosed OCs are high-grade serous carcinomas (HGSC), and the majority of these cancers are diagnosed at late stage, stage III or IV (51% and 29% respectively), with widespread disease dissemination. In addition, a significant number of patients develop malignant ascites. A key feature of tumor cells in present in ascites is the capacity to form multicellular aggregates that promote tumor cell survival and continued disease growth and spread. Cell adhesion molecule (CAM)-related downregulated by oncogenes (CDON) is a cell surface glycoprotein with established roles in normal development and associated with some tumor types. CDON signaling occurs via ligand-dependent mechanisms as a hedgehog co-receptor and ligand-independent mechanisms via interactions with cadherins. Little is known about the role of CDON in ovarian cancer, but given the importance of cadherin-mediated cell adhesion in disease dissemination, we asked whether CDON plays a role in HGSC growth and progression. We find that CDON is expressed in ovarian carcinoma (OC) cells, and protein levels are elevated by expression of oncogenes or by growth under nonadherent conditions. Depletion of CDON expression revealed that it plays an important role in HGSC cells regardless of growth conditions, resulting in decreased growth (proliferation) in cells grown under nonadherent conditions as multicellular aggregates or under adherent conditions as 2D monolayers. Alterations in signaling related to both cell adhesion and survival occur upon CDON downregulation, including decreased cadherin protein expression, integrin expression, and focal adhesion kinase activation. Depletion of CDON in OC cells results in significant tumor growth inhibition in xenograft and allograft models, suggesting that CDON plays an important role in promoting ovarian tumor growth. Treatment of OC spheroids with novel anti-CDON antibodies results in collapse of 3D cellular structures and induction of apoptosis, supporting a key role for CDON in OC cellular adhesion in tumor cell survival. These data indicate that CDON may play an essential role in OC spread since altered cell-cell adhesion and survival as multicellular aggregates is crucial for disease dissemination in patients. Taken together, our results nominate CDON as an important protein promoting HGSC growth and progression and as a novel therapeutic target for the treatment of this disease.

Citation Format: Valerie L. Sodi, Shane W. O'Brien, Chao Wu, Roland L. Dunbrack, Tiffiney R. Hartman, Alana M. O'Reilly, Denise C. Connolly. Cell adhesion molecule (CAM)-related downregulated by oncogenes (CDON) promotes ovarian cancer adhesion and survival [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr A24.

Multifaceted analysis of training and testing convolutional neural networks for protein secondary structure prediction

Protein secondary structure prediction remains a vital topic with broad applications. Due to lack of a widely accepted standard in secondary structure predictor evaluation, a fair comparison of predictors is challenging. A detailed examination of factors that contribute to higher accuracy is also lacking. In this paper, we present: (1) new test sets, Test2018, Test2019, and Test2018-2019, consisting of proteins from structures released in 2018 and 2019 with less than 25% identity to any protein published before 2018; (2) a 4-layer convolutional neural network, SecNet, with an input window of ±14 amino acids which was trained on proteins ≤25% identical to proteins in Test2018 and the commonly used CB513 test set; (3) an additional test set that shares no homologous domains with the training set proteins, according to the Evolutionary Classification of Proteins (ECOD) database; (4) a detailed ablation study where we reverse one algorithmic choice at a time in SecNet and evaluate the effect on the prediction accuracy; (5) new 4- and 5-label prediction alphabets that may be more practical for tertiary structure prediction methods. The 3-label accuracy (helix, sheet, coil) of the leading predictors on both Test2018 and CB513 is 81-82%, while SecNet's accuracy is 84% for both sets. Accuracy on the non-homologous ECOD set is only 0.6 points (83.9%) lower than the results on the Test2018-2019 set (84.5%). The ablation study of features, neural network architecture, and training hyper-parameters suggests the best accuracy results are achieved with good choices for each of them while the neural network architecture is not as critical as long as it is not too simple. Protocols for generating and using unbiased test, validation, and training sets are provided. Our data sets, including input features and assigned labels, and SecNet software including third-party dependencies and databases, are downloadable from dunbrack.fccc.edu/ss and github.com/sh-maxim/ss.

A Structurally-Validated Multiple Sequence Alignment of 497 Human Protein Kinase Domains

Studies on the structures and functions of individual kinases have been used to understand the biological properties of other kinases that do not yet have experimental structures. The key factor in accurate inference by homology is an accurate sequence alignment. We present a parsimonious, structure-based multiple sequence alignment (MSA) of 497 human protein kinase domains excluding atypical kinases. The alignment is arranged in 17 blocks of conserved regions and unaligned blocks in between that contain insertions of varying lengths present in only a subset of kinases. The aligned blocks contain well-conserved elements of secondary structure and well-known functional motifs, such as the DFG and HRD motifs. From pairwise, all-against-all alignment of 272 human kinase structures, we estimate the accuracy of our MSA to be 97%. The remaining inaccuracy comes from a few structures with shifted elements of secondary structure, and from the boundaries of aligned and unaligned regions, where compromises need to be made to encompass the majority of kinases. A new phylogeny of the protein kinase domains in the human genome based on our alignment indicates that ten kinases previously labeled as "OTHER" can be confidently placed into the CAMK group. These kinases comprise the Aurora kinases, Polo kinases, and calcium/calmodulin-dependent kinase kinases.

Protein modeling to assess the pathogenicity of rare variants of SERPINA1 in patients suspected of having Alpha 1 Antitrypsin Deficiency

Background

Alpha 1 Antitrypsin (AAT) is a key serum proteinase inhibitor encoded by SERPINA1. Sequence variants of the gene can cause Alpha 1 Antitrypsin Deficiency (AATD), a condition associated with lung and liver disease. The majority of AATD cases are caused by the ‘Z’ and ‘S’ variants – single-nucleotide variations (SNVs) that result in amino acid substitutions of E342K and E264V. However, SERPINA1 is highly polymorphic, with numerous potentially clinically relevant variants reported. Novel variants continue to be discovered, and without reports of pathogenicity, it can be difficult for clinicians to determine the best course of treatment.

Methods

We assessed the utility of next-generation sequencing (NGS) and predictive computational analysis to guide the diagnosis of patients suspected of having AATD. Blood samples on serum separator cards were submitted to the DNA₁ Advanced Screening Program (Biocerna LLC, Fulton, Maryland, USA) by physicians whose patients were suspected of having AATD. Laboratory analyses included quantification of serum AAT levels, qualitative analysis by isoelectric focusing, and targeted genotyping and NGS of the SERPINA1 gene. Molecular modeling software UCSF Chimera (University College of San Francisco, CA) was used to visualize the positions of amino acid changes as a result of rare/novel SNVs. Predictive software was used to assess the potential pathogenicity of these variants; methods included a support vector machine (SVM) program, PolyPhen-2 (Harvard University, Cambridge, MA), and FoldX (Centre for Genomic Regulation, Barcelona, Spain).

Results

Samples from 23 patients were analyzed; 21 rare/novel sequence variants were identified by NGS, including splice variants (n = 2), base pair deletions (n = 1), stop codon insertions (n = 2), and SNVs (n = 16). Computational modeling of protein structures caused by the novel SNVs showed that 8 were probably deleterious, and two were possibly deleterious. For the majority of probably/possibly deleterious SNVs (I50N, P289S, M385T, M221T, D341V, V210E, P369H, V333M and A142D), the mechanism is probably via disruption of the packed hydrophobic core of AAT. Several deleterious variants occurred in combination with more common deficiency alleles, resulting in very low AAT levels.

Conclusions

NGS and computational modeling are useful tools that can facilitate earlier, more precise diagnosis, and consideration for AAT therapy in AATD.

Electronic supplementary material

The online version of this article (10.1186/s12881-019-0852-5) contains supplementary material, which is available to authorized users.

Interaction of germline variants in a family with a history of early-onset clear cell renal cell carcinoma

Background

Identification of genetic factors causing predisposition to renal cell carcinoma has helped improve screening, early detection, and patient survival.

Methods

We report the characterization of a proband with renal and thyroid cancers and a family history of renal and other cancers by whole‐exome sequencing (WES), coupled with WES analysis of germline DNA from additional affected and unaffected family members.

Results

This work identified multiple predicted protein‐damaging variants relevant to the pattern of inherited cancer risk. Among these, the proband and an affected brother each had a heterozygous Ala45Thr variant in SDHA, a component of the succinate dehydrogenase (SDH) complex. SDH defects are associated with mitochondrial disorders and risk for various cancers; immunochemical analysis indicated loss of SDHB protein expression in the patient’s tumor, compatible with SDH deficiency. Integrated analysis of public databases and structural predictions indicated that the two affected individuals also had additional variants in genes including TGFB2, TRAP1, PARP1, and EGF, each potentially relevant to cancer risk alone or in conjunction with the SDHA variant. In addition, allelic imbalances of PARP1 and TGFB2 were detected in the tumor of the proband.

Conclusion

Together, these data suggest the possibility of risk associated with interaction of two or more variants.

Mouse modeling and structural analysis of the p.G307S mutation in human cystathionine β-synthase (CBS) reveal effects on CBS activity but not stability

Mutations in the cystathionine β-synthase (CBS) gene are the cause of classical homocystinuria, the most common inborn error in sulfur metabolism. The p.G307S mutation is the most frequent cause of CBS deficiency in Ireland, which has the highest prevalence of CBS deficiency in Europe. Individuals homozygous for this mutation tend to be severely affected and are pyridoxine nonresponsive, but the molecular basis for the strong effects of this mutation is unclear. Here, we characterized a transgenic mouse model lacking endogenous Cbs and expressing human p.G307S CBS protein from a zinc-inducible metallothionein promoter (Tg-G307S Cbs^-/-). Unlike mice expressing other mutant CBS alleles, the Tg-G307S transgene could not efficiently rescue neonatal lethality of Cbs^-/- in a C57BL/6J background. In a C3H/HeJ background, zinc-induced Tg-G307S Cbs^-/- mice expressed high levels of p.G307S CBS in the liver, and this protein variant forms multimers, similarly to mice expressing WT human CBS. However, the p.G307S enzyme had no detectable residual activity. Moreover, treating mice with proteasome inhibitors failed to significantly increase CBS-specific activity. These findings indicated that the G307S substitution likely affects catalytic function as opposed to causing a folding defect. Using molecular dynamics simulation techniques, we found that the G307S substitution likely impairs catalytic function by limiting the ability of the tyrosine at position 308 to assume the proper conformational state(s) required for the formation of the pyridoxal-cystathionine intermediate. These results indicate that the p.G307S CBS is stable but enzymatically inert and therefore unlikely to respond to chaperone-based therapy.

RosettaAntibodyDesign (RAbD): A general framework for computational antibody design

A structural-bioinformatics-based computational methodology and framework have been developed for the design of antibodies to targets of interest. RosettaAntibodyDesign (RAbD) samples the diverse sequence, structure, and binding space of an antibody to an antigen in highly customizable protocols for the design of antibodies in a broad range of applications. The program samples antibody sequences and structures by grafting structures from a widely accepted set of the canonical clusters of CDRs (North et al., J. Mol. Biol., 406:228-256, 2011). It then performs sequence design according to amino acid sequence profiles of each cluster, and samples CDR backbones using a flexible-backbone design protocol incorporating cluster-based CDR constraints. Starting from an existing experimental or computationally modeled antigen-antibody structure, RAbD can be used to redesign a single CDR or multiple CDRs with loops of different length, conformation, and sequence. We rigorously benchmarked RAbD on a set of 60 diverse antibody-antigen complexes, using two design strategies-optimizing total Rosetta energy and optimizing interface energy alone. We utilized two novel metrics for measuring success in computational protein design. The design risk ratio (DRR) is equal to the frequency of recovery of native CDR lengths and clusters divided by the frequency of sampling of those features during the Monte Carlo design procedure. Ratios greater than 1.0 indicate that the design process is picking out the native more frequently than expected from their sampled rate. We achieved DRRs for the non-H3 CDRs of between 2.4 and 4.0. The antigen risk ratio (ARR) is the ratio of frequencies of the native amino acid types, CDR lengths, and clusters in the output decoys for simulations performed in the presence and absence of the antigen. For CDRs, we achieved cluster ARRs as high as 2.5 for L1 and 1.5 for H2. For sequence design simulations without CDR grafting, the overall recovery for the native amino acid types for residues that contact the antigen in the native structures was 72% in simulations performed in the presence of the antigen and 48% in simulations performed without the antigen, for an ARR of 1.5. For the non-contacting residues, the ARR was 1.08. This shows that the sequence profiles are able to maintain the amino acid types of these conserved, buried sites, while recovery of the exposed, contacting residues requires the presence of the antigen-antibody interface. We tested RAbD experimentally on both a lambda and kappa antibody-antigen complex, successfully improving their affinities 10 to 50 fold by replacing individual CDRs of the native antibody with new CDR lengths and clusters.

BRCA2, EGFR, and NTRK mutations in mismatch repair-deficient colorectal cancers with MSH2 or MLH1 mutations

Deficient mismatch repair (MMR) and microsatellite instability (MSI) contribute to ~15% of colorectal cancer (CRCs). We hypothesized MSI leads to mutations in DNA repair proteins including BRCA2 and cancer drivers including EGFR. We analyzed mutations among a discovery cohort of 26 MSI-High (MSI-H) and 558 non-MSI-H CRCs profiled at Caris Life Sciences. Caris-profiled MSI-H CRCs had high mutation rates (50% vs 14% in non-MSI-H, P < 0.0001) in BRCA2. Of 1104 profiled CRCs from a second cohort (COSMIC), MSH2/MLH1-mutant CRCs showed higher mutation rates in BRCA2 compared to non-MSH2/MLH1-mutant tumors (38% vs 6%, P < 0.0000001). BRCA2 mutations in MSH2/MLH1-mutant CRCs included 75 unique mutations not known to occur in breast or pancreatic cancer per COSMIC v73. Only 5 deleterious BRCA2 mutations in CRC were previously reported in the BIC database as germ-line mutations in breast cancer. Some BRCA2 mutations were predicted to disrupt interactions with partner proteins DSS1 and RAD51. Some CRCs harbored multiple BRCA2 mutations. EGFR was mutated in 45.5% of MSH2/MLH1-mutant and 6.5% of non-MSH2/MLH1-mutant tumors (P < 0.0000001). Approximately 15% of EGFR mutations found may be actionable through TKI therapy, including N700D, G719D, T725M, T790M, and E884K. NTRK gene mutations were identified in MSH2/MLH1-mutant CRC including NTRK1 I699V, NTRK2 P716S, and NTRK3 R745L. Our findings have clinical relevance regarding therapeutic targeting of BRCA2 vulnerabilities, EGFR mutations or other identified oncogenic drivers such as NTRK in MSH2/MLH1-mutant CRCs or other tumors with mismatch repair deficiency.

The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design

Over the past decade, the Rosetta biomolecular modeling suite has informed diverse biological questions and engineering challenges ranging from interpretation of low-resolution structural data to design of nanomaterials, protein therapeutics, and vaccines. Central to Rosetta's success is the energy function: a model parametrized from small-molecule and X-ray crystal structure data used to approximate the energy associated with each biomolecule conformation. This paper describes the mathematical models and physical concepts that underlie the latest Rosetta energy function, called the Rosetta Energy Function 2015 (REF15). Applying these concepts, we explain how to use Rosetta energies to identify and analyze the features of biomolecular models. Finally, we discuss the latest advances in the energy function that extend its capabilities from soluble proteins to also include membrane proteins, peptides containing noncanonical amino acids, small molecules, carbohydrates, nucleic acids, and other macromolecules.

Functional analysis of rare variants in mismatch repair proteins augments results from computation-based predictive methods

The cancer-predisposing Lynch Syndrome (LS) arises from germline mutations in DNA mismatch repair (MMR) genes, predominantly MLH1, MSH2, MSH6, and PMS2. A major challenge for clinical diagnosis of LS is the frequent identification of variants of uncertain significance (VUS) in these genes, as it is often difficult to determine variant pathogenicity, particularly for missense variants. Generic programs such as SIFT and PolyPhen-2, and MMR gene-specific programs such as PON-MMR and MAPP-MMR, are often used to predict deleterious or neutral effects of VUS in MMR genes. We evaluated the performance of multiple predictive programs in the context of functional biologic data for 15 VUS in MLH1, MSH2, and PMS2. Using cell line models, we characterized VUS predicted to range from neutral to pathogenic on mRNA and protein expression, basal cellular viability, viability following treatment with a panel of DNA-damaging agents, and functionality in DNA damage response (DDR) signaling, benchmarking to wild-type MMR proteins. Our results suggest that the MMR gene-specific classifiers do not always align with the experimental phenotypes related to DDR. Our study highlights the importance of complementary experimental and computational assessment to develop future predictors for the assessment of VUS.

Benchmarking predictions of allostery in liver pyruvate kinase in CAGI4

The Critical Assessment of Genome Interpretation (CAGI) is a global community experiment to objectively assess computational methods for predicting phenotypic impacts of genomic variation. One of the 2015-2016 competitions focused on predicting the influence of mutations on the allosteric regulation of human liver pyruvate kinase. More than 30 different researchers accessed the challenge data. However, only four groups accepted the challenge. Features used for predictions ranged from evolutionary constraints, mutant site locations relative to active and effector binding sites, and computational docking outputs. Despite the range of expertise and strategies used by predictors, the best predictions were marginally greater than random for modified allostery resulting from mutations. In contrast, several groups successfully predicted which mutations severely reduced enzymatic activity. Nonetheless, poor predictions of allostery stands in stark contrast to the impression left by more than 700 PubMed entries identified using the identifiers "computational + allosteric." This contrast highlights a specialized need for new computational tools and utilization of benchmarks that focus on allosteric regulation.

Modeling and docking of antibody structures with Rosetta

We describe Rosetta-based computational protocols for predicting the 3D structure of an antibody from sequence (RosettaAntibody) and then docking the antibody to protein antigens (SnugDock). Antibody modeling leverages canonical loop conformations to graft large segments from experimentally determined structures, as well as offering (i) energetic calculations to minimize loops, (ii) docking methodology to refine the V_L-V_H relative orientation and (iii) de novo prediction of the elusive complementarity determining region (CDR) H3 loop. To alleviate model uncertainty, antibody-antigen docking resamples CDR loop conformations and can use multiple models to represent an ensemble of conformations for the antibody, the antigen or both. These protocols can be run fully automated via the ROSIE web server (http://rosie.rosettacommons.org/) or manually on a computer with user control of individual steps. For best results, the protocol requires roughly 1,000 CPU-hours for antibody modeling and 250 CPU-hours for antibody-antigen docking. Tasks can be completed in under a day by using public supercomputers.

Systematic evaluation of underlying defects in DNA repair as an approach to case-only assessment of familial prostate cancer

Risk assessment for prostate cancer is challenging due to its genetic heterogeneity. In this study, our goal was to develop an operational framework to select and evaluate gene variants that may contribute to familial prostate cancer risk. Drawing on orthogonal sources, we developed a candidate list of genes relevant to prostate cancer, then analyzed germline exomes from 12 case-only prostate cancer patients from high-risk families to identify patterns of protein-damaging gene variants. We described an average of 5 potentially disruptive variants in each individual and annotated them in the context of public databases representing human variation. Novel damaging variants were found in several genes of relevance to prostate cancer. Almost all patients had variants associated with defects in DNA damage response. Many also had variants linked to androgen signaling. Treatment of primary T-lymphocytes from these prostate cancer patients versus controls with DNA damaging agents showed elevated levels of the DNA double strand break (DSB) marker γH2AX (p < 0.05), supporting the idea of an underlying defect in DNA repair. This work suggests the value of focusing on underlying defects in DNA damage in familial prostate cancer risk assessment and demonstrates an operational framework for exome sequencing in case-only prostate cancer genetic evaluation.

Abstract 5286: A pipeline for developing a novel, predictive tool to classify variants of uncertain significance (VUS) in Lynch Syndrome

Lynch syndrome (LS) highly predisposes individuals and their families to an increased risk of colorectal cancers and to cancers of the endometrium, ovary, gastrointestinal tract, pancreas, upper urinary tract, and other tissues. The occurrence of LS has been attributed to germline, heterozygous mutations in four genes in the DNA mismatch repair (MMR) pathway: MLH1, MSH2, MSH6 and PMS2. Mutations in the MLH1 and MSH2 genes account for the majority of detectable mutations in LS, with more infrequent mutations in MSH6 and PMS2. Clinical diagnosis of LS helps direct the management of the disease and risk assessment for future cancers in the family.

A major challenge is the determination of variant pathogenicity in MMR genes, particularly those that code for missense mutations. According the InSiGHT database, there are &gt;5000 unclassified variants of unknown significance (VUS), and this number continues to grow every day due to the increased prevalence of genomic testing. Such VUS present an enormous challenge to effective genetic counseling of LS families. There are numerous predictive methods, such as SIFT and Polyphen-2, for predicting the effects of missense mutations in any gene including LS genes. Further, there is a great deal of promise for gene-specific predictors, such as PON-MMR and CoDP, that are trained on the physical and evolutionary features of known deleterious and benign mutations in the target genes of interest.

Since the number of available mutations for the LS genes with experimental phenotypes is limited, we have developed and applied a pipeline to perform experimental validation of VUS in MMR genes. MMR genes with introduced VUS were assessed in vitro in comparison to wild type genes for the effect of the VUS on steady state protein expression, localization, and functionality in inducing DNA damage checkpoints and influencing cell survival following treatment with a panel of DNA-damaging agents. We then assessed a panel of VUS in MSH2, MLH1 and PMS2 in HEK293 kidney cells and colorectal cancer (CRC) cell lines bearing deletions in the MMR genes being tested.

This mid-throughput pipeline allowed effective validation of the functional consequences of each VUS, generating additional data to enhance the efficacy of gene-specific predictors for the LS genes that we are developing. Such tools may greatly benefit the assessment of MMR gene variants identified through genetic testing and for genetic counseling of LS families.

Citation Format: Sanjeevani Arora, Peter J. Huwe, Rahmat Sikder, Manali Shah, Sanat Deshpande, Michael J. Hall, Roland L. Dunbrack, Erica A. Golemis. A pipeline for developing a novel, predictive tool to classify variants of uncertain significance (VUS) in Lynch Syndrome. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5286.

Assessment of refinement of template-based models in CASP11

CASP11 (the 11th Meeting on the Critical Assessment of Protein Structure Prediction) ran a blind experiment in the refinement of protein structure predictions, the fourth such experiment since CASP8. As with the previous experiments, the predictors were provided with one starting structure from the server models of each of a selected set of template-based modeling targets and asked to refine the coordinates of the starting structure toward native. We assessed the refined structures with the Z-scores of the standard CASP measures, which compare the model-target similarities of the models from all the predictors. Furthermore, we assessed the refined structures with "relative measures," which compare the improvement in accuracy of each model with respect to the starting structure. The latter provides an assessment of the extent to which each predictor group is able to improve the starting structures toward native. We utilized heat maps to display improvements in the Calpha-Calpha distance matrix for each model. The heat maps labeled with each element of secondary structure helped us to identify regions of refinement toward native in each model. Most positively scoring models show modest improvements in multiple regions of the structure, while in some models we were able to identify significant repositioning of N/C-terminal segments and internal elements of secondary structure. The best groups were able to improve more than 70% of the targets from the starting models, and by an average of 3-5% in the standard CASP measures. Proteins 2016; 84(Suppl 1):260-281. © 2016 Wiley Periodicals, Inc.

Biological function derived from predicted structures in CASP11

In CASP11, the organizers sought to bring the biological inferences from predicted structures to the fore. To accomplish this, we assessed the models for their ability to perform quantifiable tasks related to biological function. First, for 10 targets that were probable homodimers, we measured the accuracy of docking the models into homodimers as a function of GDT-TS of the monomers, which produced characteristic L-shaped plots. At low GDT-TS, none of the models could be docked correctly as homodimers. Above GDT-TS of ∼60%, some models formed correct homodimers in one of the largest docked clusters, while many other models at the same values of GDT-TS did not. Docking was more successful when many of the templates shared the same homodimer. Second, we docked a ligand from an experimental structure into each of the models of one of the targets. Docking to the models with two different programs produced poor ligand RMSDs with the experimental structure. Measures that evaluated similarity of contacts were reasonable for some of the models, although there was not a significant correlation with model accuracy. Finally, we assessed whether models would be useful in predicting the phenotypes of missense mutations in three human targets by comparing features calculated from the models with those calculated from the experimental structures. The models were successful in reproducing accessible surface areas but there was little correlation of model accuracy with calculation of FoldX evaluation of the change in free energy between the wild-type and the mutant. Proteins 2016; 84(Suppl 1):370-391. © 2016 Wiley Periodicals, Inc.

Assessment of template-based modeling of protein structure in CASP11

We present the assessment of predictions submitted in the template-based modeling (TBM) category of CASP11 (Critical Assessment of Protein Structure Prediction). Model quality was judged on the basis of global and local measures of accuracy on all atoms including side chains. The top groups on 39 human-server targets based on model 1 predictions were LEER, Zhang, LEE, MULTICOM, and Zhang-Server. The top groups on 81 targets by server groups based on model 1 predictions were Zhang-Server, nns, BAKER-ROSETTASERVER, QUARK, and myprotein-me. In CASP11, the best models for most targets were equal to or better than the best template available in the Protein Data Bank, even for targets with poor templates. The overall performance in CASP11 is similar to the performance of predictors in CASP10 with slightly better performance on the hardest targets. For most targets, assessment measures exhibited bimodal probability density distributions. Multi-dimensional scaling of an RMSD matrix for each target typically revealed a single cluster with models similar to the target structure, with a mode in the GDT-TS density between 40 and 90, and a wide distribution of models highly divergent from each other and from the experimental structure, with density mode at a GDT-TS value of ∼20. The models in this peak in the density were either compact models with entirely the wrong fold, or highly non-compact models. The results argue for a density-driven approach in future CASP TBM assessments that accounts for the bimodal nature of these distributions instead of Z scores, which assume a unimodal, Gaussian distribution. Proteins 2016; 84(Suppl 1):200-220. © 2016 Wiley Periodicals, Inc.

Cancer Signature Investigation: ERBB2 (HER2)-Activating Mutation and Amplification-Positive Breast Carcinoma Mimicking Lung Primary

Next-generation sequencing of primary and metachronous metastatic cancer lesions may impact patient care. We present a case of relapsed metastatic breast cancer with a dominant pulmonary lesion originally identified as lung adenocarcinoma. A 72-year-old, never-smoker woman with a protracted cough was found to have a large lung mass and regional lymphadenopathy on a chest CT. Lung mass biopsy showed adenocarcinoma with focal TTF-1 (thyroid transcription factor 1) positivity, favoring a lung primary. In addition to stereotactic brain radiation for cerebral metastases, she was started on carboplatin/pemetrexed. As part of the workup, the tumor was analyzed by a 50-gene targeted mutation panel, which detected 3 somatic mutations: ERBB2 (HER2) D769H activating missense mutation, TP53 Y126 inactivating truncating mutation, and SMARCB1 R374Q missense mutation. Of note, the patient had a history of stage IIA triple-negative grade 3 invasive ductal carcinoma of the left breast 1.5 years ago and received neoadjuvant chemotherapy and adjuvant radiation, and underwent a lumpectomy. Further analysis of her primary breast tumor showed a mutational profile identical to that of the lung tumor. Fluorescence in situ hybridization revealed HER2 amplification in the lung tumor, with a HER2/CEP17 ratio of 3.9. The patient was diagnosed with recurrent HER2-positive metastatic breast carcinoma with a coexisting ERBB2 (HER2) activating mutation. Chemotherapy was adjusted to include dual HER2-targeted therapy containing trastuzumab and pertuzumab, resulting in an ongoing partial response. This case demonstrates that a unique genetic mutational profile can clarify whether a tumor represents a metastatic lesion or new malignancy when conventional morphological and immunohistochemical methods are indeterminate, and can directly impact treatment decisions.

CASP 11 target classification

Protein target structures for the Critical Assessment of Structure Prediction round 11 (CASP11) and CASP ROLL were split into domains and classified into categories suitable for assessment of template-based modeling (TBM) and free modeling (FM) based on their evolutionary relatedness to existing structures classified by the Evolutionary Classification of Protein Domains (ECOD) database. First, target structures were divided into domain-based evaluation units. Target splits were based on the domain organization of available templates as well as the performance of servers on whole targets compared to split target domains. Second, evaluation units were classified into TBM and FM categories using a combination of measures that evaluate prediction quality and template detectability. Generally, target domains with sequence-related templates and good server prediction performance were classified as TBM, whereas targets without sequence-identifiable templates and low server performance were classified as FM. As in previous CASP experiments, the boundaries for classification were blurred due to the presence of significant insertions and deteriorations in the targets with respect to homologous templates, as well as the presence of templates with partial coverage of new folds. The FM category included 45 target domains, which represents an unprecedented number of difficult CASP targets provided for modeling. Proteins 2016; 84(Suppl 1):20-33. © 2016 Wiley Periodicals, Inc.

Identifying three-dimensional structures of autophosphorylation complexes in crystals of protein kinases

Protein kinase autophosphorylation is a common regulatory mechanism in cell signaling pathways. Crystal structures of several homomeric protein kinase complexes have a serine, threonine, or tyrosine autophosphorylation site of one kinase monomer located in the active site of another monomer, a structural complex that we call an "autophosphorylation complex." We developed and applied a structural bioinformatics method to identify all such autophosphorylation complexes in x-ray crystallographic structures in the Protein Data Bank (PDB). We identified 15 autophosphorylation complexes in the PDB, of which five complexes had not previously been described in the publications describing the crystal structures. These five complexes consist of tyrosine residues in the N-terminal juxtamembrane regions of colony-stimulating factor 1 receptor (CSF1R, Tyr(561)) and ephrin receptor A2 (EPHA2, Tyr(594)), tyrosine residues in the activation loops of the SRC kinase family member LCK (Tyr(394)) and insulin-like growth factor 1 receptor (IGF1R, Tyr(1166)), and a serine in a nuclear localization signal region of CDC-like kinase 2 (CLK2, Ser(142)). Mutations in the complex interface may alter autophosphorylation activity and contribute to disease; therefore, we mutated residues in the autophosphorylation complex interface of LCK and found that two mutations impaired autophosphorylation (T445V and N446A) and mutation of Pro(447) to Ala, Gly, or Leu increased autophosphorylation. The identified autophosphorylation sites are conserved in many kinases, suggesting that, by homology, these complexes may provide insight into autophosphorylation complex interfaces of kinases that are relevant drug targets.

Stability engineering of anti-EGFR scFv antibodies by rational design of a lambda-to-kappa swap of the VL framework using a structure-guided approach

Phage-display technology facilitates rapid selection of antigen-specific single-chain variable fragment (scFv) antibodies from large recombinant libraries. ScFv antibodies, composed of a VH and VL domain, are readily engineered into multimeric formats for the development of diagnostics and targeted therapies. However, the recombinant nature of the selection strategy can result in VH and VL domains with sub-optimal biophysical properties, such as reduced thermodynamic stability and enhanced aggregation propensity, which lead to poor production and limited application. We found that the C10 anti-epidermal growth factor receptor (EGFR) scFv, and its affinity mutant, P2224, exhibit weak production from E. coli. Interestingly, these scFv contain a fusion of lambda3 and lambda1 V-region (LV3 and LV1) genes, most likely the result of a PCR aberration during library construction. To enhance the biophysical properties of these scFvs, we utilized a structure-based approach to replace and redesign the pre-existing framework of the VL domain to one that best pairs with the existing VH. We describe a method to exchange lambda sequences with a more stable kappa3 framework (KV3) within the VL domain that incorporates the original lambda DE-loop. The resulting scFvs, C10KV3_LV1DE and P2224KV3_LV1DE, are more thermodynamically stable and easier to produce from bacterial culture. Additionally, C10KV3_LV1DE and P2224KV3_LV1DE retain binding affinity to EGFR, suggesting that such a dramatic framework swap does not significantly affect scFv binding. We provide here a novel strategy for redesigning the light chain of problematic scFvs to enhance their stability and therapeutic applicability.

Abstract 537: Information-driven approaches to predicting familial risk for prostate cancer

Given the known genetic heterogeneity of prostate cancer, risk assessment for this disease is a field that is likely to be positively impacted by improved interpretation of genetic variants identified through exome sequencing. At present, there is insufficient evidence to base decisions regarding screening, diagnosis and management of prostate cancer on genetic tests. While a few genes (such as BRCA2 and HOXB13) are definitively linked to prostate cancer risk, they are not found in sizeable patient populations. However, a growing number of candidates for prostate cancer risk have been proposed. As information resources including the TCGA and other consortium databases expand, clinicians and patients will face issues with how to interpret genetic data variant regarding cancer risk. The goal of the present work is to develop a paradigm that addresses the plausible scenario of a clinician seeking to provide prostate cancer genetic risk assessment for patients with personal and/or a family history of one or more types of cancer, but for whom access to relative specimens for sequencing analysis is highly limited. In particular, we focus on the development of an operational framework through which to select and evaluate germ-line variants which may contribute to familial cancer risk.

Our study evaluates the current potential of integrated informatics resources to help identify clinically informative variants following exome sequencing of germ-line DNA from 12 prostate cancer patients with elevated risk of prostate or other cancers based on family history. We explored variants in genes meeting specific criteria: 1) genes thought to be relevant to hereditary prostate cancer, or experimentally validated as regulating the growth of prostate tumors in cell or animal models; 2) genes affected by somatic changes in cancer; 3) genes involved in DNA damage repair (DDR) including recently described androgen-regulated DDR genes; and 4) genes involved in disorders of glycosylation. We first filtered exome data based on the observed frequency of gene variants in the general population. We then utilized five independent in silico predictors and expert analysis of protein structure and interactions to identify function-disrupting rare variants. We described an average of 4 potentially disruptive variants in each individual and annotated them in the context of the human variation data accumulated over the last few years and represented in various public databases. Novel variants were found in PALB2, RAD54L2, HSD3B1, NRIP1, SCN11A, CYPBP1, SULT1E1 and UBE2D3. Molecular modeling of a p.(S221N) mutation in the steroid binding site of the aldo-keto reductase AKR1C1 showed it would be predicted to disrupt the active site, influencing androgen metabolism. Our study highlights the need and potential for well-curated databases of variants with clinical relevance that will ultimately facilitate germ-line exome testing in the clinical setting.

Citation Format: Emmanuelle Nicolas, Yan Zhou, Ilya G. Serebriiskii, Mark D. Andrake, Elizabeth A. Handorf, Roland L. Dunbrack, Veda N. Giri, Eric A. Ross, Erica A. Golemis, Michael J. Hall, Mary B. Daly. Information-driven approaches to predicting familial risk for prostate cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 537. doi:10.1158/1538-7445.AM2015-537

Abstract LB-034: Identification of novel autophosphorylation structures in crystals of protein kinases

Cancer therapy depends heavily on the ability to effectively control the activity of oncogenic kinases. Autophosphorylation is a common regulatory mechanism of kinases in signaling pathways, and commonly elevated in cancer. Several autophosphorylation complexes have been identified from within crystals of protein kinases, with a known autophosphorylation site of one kinase monomer sitting in the active site of another monomer of the same protein in the crystal. We have utilized a structural bioinformatics method to identify all such autophosphorylation complexes in X-ray crystal structures in the Protein Data Bank by generating all unique kinase/kinase interfaces within and between asymmetric units of each crystal and measuring the distance between the hydroxyl oxygen of the autophosphorylation sites and the oxygen atoms of the active site aspartic acid residue side chain. With this approach, we have identified 15 autophosphorylation complexes in the PDB, of which 5 complexes have not previously been described.

Of greatest interest are five structures of activation loop autophosphorylation - PAK1 (T423), IRAK4 (T345), IGF1R (Y1165 and Y1166), and LCK (Y394), two of which we have identified for the first time (IGF1R-Y1166 and LCK). We show that 269 human kinases have potential S/T or Y phosphorylation sites at positions analogous to the PAK1, IGF1R-Y1165/LCK, and IGF1R-Y1166 structures, and that there are 182 such positions that are annotated as phosphorylation sites in Uniprot. To assess the functional importance of the LCK dimer, we performed mutational analysis of residues in the autophosphorylation complex interface of LCK and found that mutations disrupting the interface either severely impaired autophosphorylation (T445D and N446D) or increased it (P447L,A,G). The P447L mutation has been previously found in a T-cell leukemia cell line and associated with activation of LCK.

Three structures of receptor tyrosine kinases contain autophosphorylation complexes of the juxtamembrane segment just N-terminal to the kinase domain, two of which are identified for the first time. One of these, CSF1R (Y561) is a homologous site to a known c-KIT (Y568) autophosphorylation structure. The other is in EPHA2 (Y594), which is homologous to Y570 in c-KIT, which is also an autophosphorylation site. Twenty receptor tyrosine kinases contain autophosphorylation sites at one or both of these positions. Phosphorylation at these sites is associated with interaction with SRC family kinases and other downstream effectors of receptor tyrosine kinase signaling.

These structures provide critical information on domain-domain interactions and substrate specificity in autophosphorylation, as well as opportunities for understanding the role of certain cancer driver mutations and the development of non-ATP-competitive inhibitors that block dimerization shown in these structures.

Note: This abstract was not presented at the meeting.

Citation Format: Qifang Xu, Kimberly Malecka, Jeffrey Peterson, Roland L. Dunbrack. Identification of novel autophosphorylation structures in crystals of protein kinases. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr LB-034. doi:10.1158/1538-7445.AM2015-LB-034

Defects in DNA Repair Genes Predict Response to Neoadjuvant Cisplatin-based Chemotherapy in Muscle-invasive Bladder Cancer

BACKGROUND

Cisplatin-based neoadjuvant chemotherapy (NAC) before cystectomy is the standard of care for muscle-invasive bladder cancer (MIBC), with 25-50% of patients expected to achieve a pathologic response. Validated biomarkers predictive of response are currently lacking.

OBJECTIVE

To discover and validate biomarkers predictive of response to NAC for MIBC.

DESIGN, SETTING, AND PARTICIPANTS

Pretreatment MIBC samples prospectively collected from patients treated in two separate clinical trials of cisplatin-based NAC provided the discovery and validation sets. DNA from pretreatment tumor tissue was sequenced for all coding exons of 287 cancer-related genes and was analyzed for base substitutions, indels, copy number alterations, and selected rearrangements in a Clinical Laboratory Improvements Amendments-certified laboratory.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The mean number of variants and variant status for each gene were correlated with response. Variant data from the discovery cohort were used to create a classification tree to discriminate responders from nonresponders. The resulting decision rule was then tested in the independent validation set.

RESULTS AND LIMITATIONS

Patients with a pathologic complete response had more alterations than those with residual tumor in both the discovery (p=0.024) and validation (p=0.018) sets. In the discovery set, alteration in one or more of the three DNA repair genes ATM, RB1, and FANCC predicted pathologic response (p<0.001; 87% sensitivity, 100% specificity) and better overall survival (p=0.007). This test remained predictive for pathologic response in the validation set (p=0.033), with a trend towards better overall survival (p=0.055). These results require further validation in additional sample sets.

CONCLUSIONS

Genomic alterations in the DNA repair-associated genes ATM, RB1, and FANCC predict response and clinical benefit after cisplatin-based chemotherapy for MIBC. The results suggest that defective DNA repair renders tumors sensitive to cisplatin.

PATIENT SUMMARY

Chemotherapy given before bladder removal (cystectomy) improves the chance of cure for some but not all patients with muscle-invasive bladder cancer. We found a set of genetic mutations that when present in tumor tissue predict benefit from neoadjuvant chemotherapy, suggesting that testing before chemotherapy may help in selecting patients for whom this approach is recommended.

Abstract 4298: Defects in DNA repair genes and sensitivity to cisplatin based neoadjuvant chemotherapy (NAC) for bladder cancer

Background: Cisplatin based NAC prior to cystectomy is standard of care for MIBC, with 40-50% expected to respond with ≤pT1N0M0. Biomarkers predictive of response are lacking.

Methods: MIBC pts who received 3 cycles of cisplatin based NAC on 1 of 2 prospective multicenter clinical trials were included. Pts treated with accelerated methotrexate, vinblastine, doxorubicin + cisplatin (AMVAC) provided the discovery set [n = 34, 15/34 (44%) ≤pT1N0M0]. Pts treated with dose dense gemcitabine + cisplatin (DDGC) provided the validation set [n = 24, 11/24 (46%) ≤pT1N0M0]. DNA from pre-treatment tumor tissue underwent sequencing for all coding exons of 287 cancer related genes and was analyzed for presence of base substitutions, indels, copy number alterations, and selected re-arrangements. The mean number of variants and variant status for each gene were correlated with response using two-sample t-test and Fisher's exact tests. Variant data were used to create a classification tree to discriminate responders vs. non-responders in the AMVAC discovery cohort. The resulting decision rule was then tested in the independent DDGC validation set. Overall survival analysis was performed using Kaplan-Meier.

Results: Pts with pT0 had significantly more alterations than those with residual tumor in both the AMVAC discovery (p = .024) and DDGC validation (p = 0.018) set. In the AMVAC discovery set, alteration in ≥1 of the three DNA repair genes ATM, RB1 or FANCC predicted for ≤pT1N0M0 (p&lt;0.001, 87% sensitivity, 100% specificity) and improved overall survival (OS) (p = 0.007). This test remained predictive for ≤pT1N0M0 in the DDGC validation set (p = 0.033), with a trend towards improved OS (p = 0.07) at short median follow up of 14.3 mo.

Conclusions: Alterations in ≥1 of ATM, RB1 and FANCC predict response to cisplatin based chemotherapy defined as ≤pT1N0M0 in both our AMVAC discovery and DDGC validation sets. We hypothesize that defects in these genes, which are important for maintenance of chromatin structure and DNA repair, confer sensitivity to DNA damaging chemotherapy and explain the accumulation of alterations seen among pts with pT0. External validation in collaboration with the cooperative groups is planned.

Citation Format: Elizabeth R. Plimack, Roland L. Dunbrack, Timothy A. Brennan, Mark D. Andrake, Yan Zhou, Ilya Serebriiskii, Essel Dulaimi Al-Saleem, Jean Hoffman-Censits, Marijo Bilusic, Yu-Ning Wong, Alexander Kutikov, Rosalia Viterbo, Richard Greenberg, David Chen, Costas D. Lallas, Edouard J. Trabulsi, Roman Yelensky, Vincent A. Miller, Erica Golemis, Eric Ross. Defects in DNA repair genes and sensitivity to cisplatin based neoadjuvant chemotherapy (NAC) for bladder cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4298. doi:10.1158/1538-7445.AM2015-4298

Characteristics of clinical trials that require participants to be fluent in English

BACKGROUND/AIMS

Diverse samples in clinical trials can make findings more generalizable. We sought to characterize the prevalence of clinical trials in the United States that required English fluency for participants to enroll in the trial.

METHODS

We randomly chose over 10,000 clinical trial protocols registered with ClinicalTrials.gov and examined the inclusion and exclusion criteria of the trials. We compared the relationship of clinical trial characteristics with English fluency inclusion requirements. We merged the ClinicalTrials.gov data with US Census and American Community Survey data to investigate the association of English-language restrictions with ZIP-code-level demographic characteristics of participating institutions. We used Chi-squared tests, t-tests, and logistic regression models for analyses.

RESULTS

English fluency requirements have been increasing over time, from 1.7% of trials having such requirements before 2000 to 9.0% after 2010 (p < 0.001 from Chi-squared test). Industry-sponsored trials had low rates of English fluency requirements (1.8%), while behavioral trials had high rates (28.4%). Trials opening in the Northeast of the United States had the highest regional English requirement rates (10.7%), while trials opening in more than one region had the lowest (3.3%, p<0.001). Since 1995, trials opening in ZIP codes with larger Hispanic populations were less likely to have English fluency requirements (odds ratio=0.92 for each 10% increase in proportion of Hispanics, 95% confidence interval=0.86-0.98, p=0.013). Trials opening in ZIP codes with more residents self-identifying as Black/African American (odds ratio=1.87, 95% confidence interval=1.36-2.58, p<0.001 for restricted cubic spline term) or Asian (odds ratio=1.16 for linear term, 95% confidence interval=1.07-1.25, p<0.001) were more likely to have English fluency requirements. ZIP codes with higher poverty rates had trials with more English-language restrictions (odds ratio=1.06 for a 10% poverty rate increase, 95% confidence interval=1.001-1.11, p=0.045). There was a statistically significant interaction between year and intervention type, such that the increase in English fluency requirements was more common for some interventions than for others.

CONCLUSION

The proportion of clinical trials registered with ClinicalTrials.gov that have English fluency requirements for study inclusion has been increasing over time. English-language restrictions are associated with a number of characteristics, including the demographic characteristics of communities in which the sponsoring institutions are located.

Conformational analysis of the DFG-out kinase motif and biochemical profiling of structurally validated type II inhibitors

Structural coverage of the human kinome has been steadily increasing over time. The structures provide valuable insights into the molecular basis of kinase function and also provide a foundation for understanding the mechanisms of kinase inhibitors. There are a large number of kinase structures in the PDB for which the Asp and Phe of the DFG motif on the activation loop swap positions, resulting in the formation of a new allosteric pocket. We refer to these structures as “classical DFG-out” conformations in order to distinguish them from conformations that have also been referred to as DFG-out in the literature but that do not have a fully formed allosteric pocket. We have completed a structural analysis of almost 200 small molecule inhibitors bound to classical DFG-out conformations; we find that they are recognized by both type I and type II inhibitors. In contrast, we find that nonclassical DFG-out conformations strongly select against type II inhibitors because these structures have not formed a large enough allosteric pocket to accommodate this type of binding mode. In the course of this study we discovered that the number of structurally validated type II inhibitors that can be found in the PDB and that are also represented in publicly available biochemical profiling studies of kinase inhibitors is very small. We have obtained new profiling results for several additional structurally validated type II inhibitors identified through our conformational analysis. Although the available profiling data for type II inhibitors is still much smaller than for type I inhibitors, a comparison of the two data sets supports the conclusion that type II inhibitors are more selective than type I. We comment on the possible contribution of the DFG-in to DFG-out conformational reorganization to the selectivity.

PyIgClassify: a database of antibody CDR structural classifications

Classification of the structures of the complementarity determining regions (CDRs) of antibodies is critically important for antibody structure prediction and computational design. We have previously performed a clustering of antibody CDR conformations and defined a systematic nomenclature consisting of the CDR, length and an integer starting from the largest to the smallest cluster in the data set (e.g. L1-11-1). We present PyIgClassify (for Python-based immunoglobulin classification; available at http://dunbrack2.fccc.edu/pyigclassify/), a database and web server that provides access to assignments of all CDR structures in the PDB to our classification system. The database includes assignments to the IMGT germline V regions for heavy and light chains for several species. For humanized antibodies, the assignment of the frameworks is to human germlines and the CDRs to the germlines of mice or other species sources. The database can be searched by PDB entry, cluster identifier and IMGT germline group (e.g. human IGHV1). The entire database is downloadable so that users may filter the data as needed for antibody structure analysis, prediction and design.

Issues in interpreting the in vivo activity of Aurora-A

INTRODUCTION

Based on its role as a mitotic regulatory kinase, overexpressed and associated with aneuploidy in cancer, small-molecule inhibitors have been developed for Aurora-A (AURKA) kinase. In preclinical and clinical assessments, these agents have shown efficacy in inducing stable disease or therapeutic response. In optimizing the use of Aurora-A inhibitors, it is critical to have robust capacity to measure the kinase activity of Aurora-A in tumors.

AREAS COVERED

We provide an overview of molecular mechanisms of mitotic and non-mitotic activation of Aurora-A kinase, and interaction of Aurora-A with its regulatory partners. Typically, Aurora-A activity is measured by use of phospho-antibodies targeting an autophosphorylated T288 epitope. However, recent studies have identified alternative means of Aurora-A activation control, including allosteric regulation by partners, phosphorylation on alternative activating residues (S51, S98), dephosphorylation on inhibitory sites (S342) and T288 phosphorylation by alternative kinases such as Pak enzymes. Additional work has shown that the relative abundance of Aurora-A partners can affect the activity of Aurora-A inhibitors, and that Aurora-A activation also occurs in interphase cells.

EXPERT OPINION

Taken together, this work suggests the need for comprehensive analysis of Aurora-A activity and expression of Aurora-A partners in order to stratify patients for likely therapeutic response.

All-atom empirical potential for molecular modeling and dynamics studies of proteins

New protein parameters are reported for the all-atom empirical energy function in the CHARMM program. The parameter evaluation was based on a self-consistent approach designed to achieve a balance between the internal (bonding) and interaction (nonbonding) terms of the force field and among the solvent-solvent, solvent-solute, and solute-solute interactions. Optimization of the internal parameters used experimental gas-phase geometries, vibrational spectra, and torsional energy surfaces supplemented with ab initio results. The peptide backbone bonding parameters were optimized with respect to data for N-methylacetamide and the alanine dipeptide. The interaction parameters, particularly the atomic charges, were determined by fitting ab initio interaction energies and geometries of complexes between water and model compounds that represented the backbone and the various side chains. In addition, dipole moments, experimental heats and free energies of vaporization, solvation and sublimation, molecular volumes, and crystal pressures and structures were used in the optimization. The resulting protein parameters were tested by applying them to noncyclic tripeptide crystals, cyclic peptide crystals, and the proteins crambin, bovine pancreatic trypsin inhibitor, and carbonmonoxy myoglobin in vacuo and in crystals. A detailed analysis of the relationship between the alanine dipeptide potential energy surface and calculated protein φ, χ angles was made and used in optimizing the peptide group torsional parameters. The results demonstrate that use of ab initio structural and energetic data by themselves are not sufficient to obtain an adequate backbone representation for peptides and proteins in solution and in crystals. Extensive comparisons between molecular dynamics simulations and experimental data for polypeptides and proteins were performed for both structural and dynamic properties. Energy minimization and dynamics simulations for crystals demonstrate that the latter are needed to obtain meaningful comparisons with experimental crystal structures. The presented parameters, in combination with the previously published CHARMM all-atom parameters for nucleic acids and lipids, provide a consistent set for condensed-phase simulations of a wide variety of molecules of biological interest.

Abstract A291: A highly selective dual insulin receptor (IR)/insulin-like growth factor 1 receptor (IGF-1R) inhibitor derived from an ERK inhibitor

Dual inhibitors of the closely related receptor tyrosine kinases insulin-like growth factor 1 receptor (IGF-1R) and insulin receptor (IR) are promising therapeutic agents in cancer. Here we report an unusually selective class of dual inhibitors of IGF-1R and IR identified in a parallel screen of known kinase inhibitors against a panel of 300 human protein kinases. Biochemical and structural studies indicate that this class achieves its high selectivity by binding to the ATP-binding pocket of inactive, unphosphorylated IGF-1R/IR and stabilizing the activation loop in a native-like inactive conformation. One member of this compound family was originally reported as an inhibitor of the serine/threonine kinase ERK, a kinase that is distinct in the structure of its unphosphorylated/inactive form from IR/IGF-1R. Remarkably, this compound binds to the ATP-binding pocket of ERK in an entirely different conformation to that of IGF-1R/IR, explaining the potency against these two structurally distinct kinase families. These findings suggest a novel approach to polypharmacology in which two or more unrelated kinases are inhibited by a single compound that targets different conformations of each target kinase.

Citation Information: Mol Cancer Ther 2013;12(11 Suppl):A291.

Citation Format: Krisna C. Duong-Ly, Theonie Anastassiadis, Sean W. Deacon, Alec Lafontant, Haiching Ma, Karthik Devarajan, Roland L. Dunbrack, Jinhua Wu, Jeffrey R. Peterson. A highly selective dual insulin receptor (IR)/insulin-like growth factor 1 receptor (IGF-1R) inhibitor derived from an ERK inhibitor. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr A291.