Although protein identification by matching tandem mass spectra (MS/MS) against protein databases is a widespread tool in mass spectrometry, the question about reliability of such searches remains open. Absence of rigorous significance scores in MS/MS database search makes it difficult to discard random database hits and may lead to erroneous protein identification, particularly in the case of mutated or post-translationally modified peptides. This problem is especially important for high-throughput MS/MS projects when the possibility of expert analysis is limited. Thus, algorithms that sort out reliable database hits from unreliable ones and identify mutated and modified peptides are sought. Most MS/MS database search algorithms rely on variations of the Shared Peaks Count approach that scores pairs of spectra by the peaks (masses) they have in common. Although this approach proved to be useful, it has a high error rate in identification of mutated and modified peptides. We describe new MS/MS database search tools, MS-CONVOLUTION and MS-ALIGNMENT, which implement the spectral convolution and spectral alignment approaches to peptide identification. We further analyze these approaches to identification of modified peptides and demonstrate their advantages over the Shared Peaks Count. We also use the spectral alignment approach as a filter in a new database search algorithm that reliably identifies peptides differing by up to two mutations/modifications from a peptide in a database.

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Background: Scientific literature is a source of the most reliable and comprehensive knowledge about molecular interaction networks. Formalization of this knowledge is necessary for computational analysis and is achieved by automatic fact extraction using various text-mining algorithms. Most of these techniques suffer from high false positive rates and redundancy of the extracted information. The extracted facts form a large network with no pathways defined.

Intravitreal ranibizumab is a first‐line therapy for neovascular age‐related macular degeneration (nAMD), but there is a need to optimize patient outcomes while minimizing treatment burden. Here, we developed an indirect response, nonlinear, mixed effects model of disease progression and drug effect in anti‐vascular endothelial growth factor (VEGF) treatment‐naïve patients. A total of 1,524 treatment‐naïve patients and 29,754 visual acuity observations from the ANCHOR, MARINA, PIER, and EXCITE clinical trials informed the model. The model accurately described natural nAMD disease progression and predicted mean visual acuity gains in the HARBOR study, notably with a 2.0 mg ranibizumab dose not used for model development. Furthermore, individualized treatment regimens were shown by simulation to be a viable alternative to the commonly used pro re nata or fixed monthly dosing regimen approaches. Therefore, this model could be a useful tool to predict the outcomes of different, more patient‐tailored treatment regimens in nAMD.

IMPORTANCE Age-related macular degeneration (AMD) is a common threat to vision loss in individuals older than 50 years. While neovascular complications in AMD are treatable, there is currently no therapy for geographic atrophy secondary to AMD. Geographic atrophy lesion progression over time shows considerable interindividual variability, but little is known about prognostic factors. OBJECTIVE To elucidate the contribution of common genetic variants to geographic atrophy lesion growth. DESIGN, SETTING, AND PARTICIPANTS This pooled analysis combined 4 independent studies: the Fundus Autofluorescence Imaging in Age-Related Macular Degeneration (FAM) study, the Directional Spread in Geographic Atrophy (DSGA) study, the Age-Related Eye Disease Study (AREDS), and the Geographic Atrophy Treatment Evaluation (GATE) study. Each provided data for geographic atrophy lesion growth in specific designs. Patients with geographic atrophy secondary to AMD were recruited to these studies. Genotypes were retrieved through the database of Genotypes and Phenotypes (for AREDS) or generated at the Cologne Center for Genomics (for FAM, DSGA, and GATE). MAIN OUTCOMES The correlation between square root-transformed geographic atrophy growth rate and 7 596 219 genetic variants passing quality control was estimated using linear regression. The calculations were adjusted for known factors influencing geographic atrophy growth, such as the presence of bilateral geographic atrophy as well as the number of lesion spots and follow-up times. MAIN OUTCOMES AND MEASURES Slopes per allele, 95% CIs, and P values of genetic variants correlated with geographic atrophy lesion growth. RESULTS A total of 935 patients (mean [SD] age, 74.7 [7.8] years; 547 female participants [59.0%]) were included. Two gene loci with conservative genome-wide significance were identified. Each minor allele of the genome-wide associated variants increased the geographic atrophy growth rate by a mean of about 15% or 0.05 mm per year. Gene prioritization within each locus suggests the protein arginine methyltransferase 6 gene (PRMT6; chromosome 1; slope, 0.046 [95% CI, 0.026-0.066]; P = 4.09 × 10 −8) and the lanosterol synthase gene (LSS; chromosome 21; slope, 0.105 [95% CI, 0.068-0.143]; P = 4.07 × 10 −7) as the most likely progression-associated genes. CONCLUSIONS AND RELEVANCE These data provide further insight into the genetic architecture of geographic atrophy lesion growth. Geographic atrophy is a clinical outcome with a high medical need for effective therapy. The genes PRMT6 and LSS are promising candidates for future studies aimed at understanding functional aspects of geographic atrophy progression and also for designing novel and targeted treatment options.