Biology subjects

Solak, A. C.

Publications and source records attributed to Solak, A. C..

5 recordsLinked to original sources

bioRxiv2024

Antibody Library Design by Seeding LinearProgramming with Inverse Folding and ProteinLanguage Models

Designing effective antibody libraries is a challenging combinatorial search problem in computational biology. We propose a novel integer linear programming (ILP) method that explicitly controls diversity and affinity objectives when generating candidate libraries. Our approach formulates library design as a constrained optimization problem, where diversity parameters and predicted binding scores are encoded as ILP constraints and objectives. Predicted binding scores are obtained via AI-guided mutational fitness profiling, which combines protein language models and inverse folding tools to evaluate mutational effects. We demonstrate the method on coldstart design tasks for Trastuzumab, D44.1, and Spesolimab, showing that our optimized libraries outperform baseline designs in both predicted affinity and sequence diversity. This hybrid search-and-learning framework illustrates how constrained optimization and predictive modeling can be combined to deliver interpretable, high-quality solutions to antibody library engineering. Code is available at https://github.com/llnl/protlib-designer. ACM Reference FormatConor F. Hayes, Andre R. Goncalves, Steven Magana-Zook, Jacob Pettit, Ahmet Can Solak, Daniel Faissol, and Mikel Landajuela. 2026. Combinatorial Optimization of Antibody Libraries via Constrained Integer Programming. In Proc. of the 25th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2026), Paphos, Cyprus, May 25 - 29, 2026, IFAAMAS, 22 pages.

bioinformatics↗

bioRxiv2024

PCM1 conveys centrosome asymmetry to polarized endosome dynamics in regulating daughter cell fate

Vertebrate radial glia progenitors (RGPs), the principal neural stem cells, balance self-renewal and differentiation through asymmetric cell division (ACD), during which unequal inheritance of centrosomes is observed. Mechanistically, how centrosome asymmetry leads to distinct daughter cell fate remains largely unknown. Here we find that the centrosome protein Pericentriolar Material 1 (Pcm1), asymmetrically distributed at the centrosomes, regulates polarized endosome dynamics and RGP fate. In vivo time-lapse imaging and nanoscale-resolution expansion microscopy of zebrafish embryonic RGPs detect Pcm1 on Notch ligand-containing endosomes, in a complex with the polarity regulator Par-3 and dynein motor. Loss of pcm1 disrupts endosome dynamics, with clonal analysis uncovering increased neuronal production at the expense of progenitors. Pcm1 facilitates an exchange of Rab5b (early) for Rab11a (recycling) endosome markers and promotes the formation of Par-3 and dynein macromolecular complexes on recycling endosomes. Finally, in human-induced pluripotent stem cell-derived brain organoids, PCM1 shows asymmetry and co-localization with PARD3 and RAB11A in mitotic neural progenitors. Our data reveal a new mechanism by which centrosome asymmetry is conveyed by Pcm1 to polarize endosome dynamics and Notch signaling in regulating ACD and progenitor fate.

cell biology↗

bioRxiv2023

Zebrahub - Multimodal Zebrafish Developmental Atlas Reveals the State Transition Dynamics of Late Vertebrate Pluripotent Axial Progenitors

Elucidating the developmental processes of organisms requires a comprehensive understanding of cellular lineages in the spatial, temporal, and molecular domains. In this study, we introduce Zebrahub, a dynamic atlas of zebrafish embryonic development that integrates single-cell sequencing time course data with lineage reconstructions facilitated by light-sheet microscopy. This atlas offers high-resolution and in-depth molecular insights into zebrafish development, achieved through the sequencing of individual embryos across ten developmental stages, complemented by trajectory reconstructions. Zebrahub also incorporates an interactive tool to navigate the complex cellular flows and lineages derived from light-sheet microscopy data, enabling in silico fate mapping experiments. To demonstrate the versatility of our multi-modal resource, we utilize Zebrahub to provide fresh insights into the pluripotency of Neuro-Mesodermal Progenitors (NMPs). Our publicly accessible web-based platform, Zebrahub, is a foundational resource for studying developmental processes at both transcriptional and spatiotemporal levels, providing researchers with an integrated approach to exploring and analyzing the complexities of cellular lineages during zebrafish embryogenesis.

developmental biology↗

bioRxiv2022

ortho_seqs: A Python tool for sequence analysis and higher order sequence-phenotype mapping

MotivationAn important goal in sequence analysis is to understand how parts of DNA, RNA, or protein sequences interact with each other and to predict how these interactions result in given phenotypes. Mapping phenotypes onto underlying sequence space at first- and higher order levels in order to independently quantify the impact of given nucleotides or residues along a sequence is critical to understanding sequence-phenotype relationships. ResultsWe developed a Python software tool, ortho_seqs, that quantifies higher order sequence-phenotype interactions based on our previously published method of applying multivariate tensor-based orthogonal polynomials to biological sequences. Using this method, nucleotide or amino acid sequence information is converted to vectors, which are then used to build and compute the first- and higher order tensor-based orthogonal polynomials. We derived a more complete version of the mathematical method that includes projections that not only quantify effects of given nucleotides at a particular site, but also identify the effects of nucleotide substitutions. We show proof of concept of this method, provide a use case example as applied to synthetic antibody sequences, and demonstrate the application of ortho_seqs to other other sequence-phenotype datasets. Availabilityhttps://github.com/snafees/ortho_seqs & documentation https://ortho-seqs.readthedocs.io/

bioinformatics↗

bioRxiv2021

ZAF -- An Open Source Fully Automated Feeder for Aquatic Facilities

In the past few decades, aquatic animals have become popular model organisms in biology, spurring a growing need for establishing aquatic facilities. Zebrafish are widely studied and relatively easy to culture using commercial systems. However, a challenging aspect of maintaining aquatic facilities is animal feeding, which is both time- and resource-consuming. We have developed an open-source fully automatic daily feeding system, Zebrafish Automatic Feeder (ZAF). ZAF is reliable, provides a standardized amount of food to every tank, is cost-efficient and easy to build. The advanced version, ZAF+, allows for the precise control of food distribution as a function of fish density per tank, and has a user-friendly interface. Both ZAF and ZAF+ are adaptable to any laboratory environment and facilitate the implementation of aquatic colonies. Here we provide all blueprints and instructions for building the mechanics, electronics, fluidics, as well as to setup the control software and its user-friendly graphical interface. Importantly, the design is modular and can be scaled to meet different user needs. Furthermore, our results show that ZAF and ZAF+ do not adversely affect zebrafish culture, enabling fully automatic feeding for any aquatic facility.

developmental biology↗