Biology subjects

Bruncsics, B.

Publications and source records attributed to Bruncsics, B..

5 recordsLinked to original sources

bioRxiv2026

DESPOT: Direction-Enhanced Scoring POTentials

Knowledge-based potentials (KBPs) remain among the most reliable and interpretable scoring functions for protein-ligand interactions, yet most share two structural limitations. They assume that the space around each protein atom is isotropic, and their interaction-conditioned reference state cannot represent regions of space that are preferentially left empty. We introduce DESPOT (Direction-Enhanced Scoring POTentials), an all-atom anisotropic KBP that overcomes both. DESPOT classifies atoms into isotropic, axially symmetric, and fully anisotropic symmetry classes from their hybridization and bonding environment, and discretizes the surrounding interaction space using the according symmetry. By adopting a positionally averaged reference state and using a void ligand atom type, it learns, for every point around a protein atom, the probability that the point is occupied by a given ligand atom type or preferentially left empty - a ligand-independent description that naturally encodes steric exclusion. This occupancy-conditioned potential captures the precise, atom-level placement of ligand atoms; we pair it with a complementary geometry-conditioned, residue-level formulation (DESPOT-screen, in the spirit of KORP-PL) and combine the two inverse-Boltzmann scores into a consensus score, DESPOT-combo. Derived from 110,943 curated, energy-minimized complexes drawn from the CROWN database and evaluated on the CASF-2016 benchmark, DESPOT achieves competitive scoring power (Pearson r = 0.61), while DESPOT-combo attains best-in-class docking power (89.5% top-1 success); all anisotropic DESPOT variants significantly outperform isotropic KBPs and established empirical scoring functions in virtual screening. Anisotropy is decisive for rejecting geometrically implausible poses, and uniting the atom-level precision of DESPOT with the implicit flexibility tolerance of the residue-level score yields the most consistent performance across tasks. Because the same occupancy-conditioned potentials can be evaluated over an empty grid, DESPOT generates molecular interaction fields as well, unifying pose scoring with direction-aware binding-site characterization within a single interpretable model.

bioinformatics↗

bioRxiv2026

CROWN: Curated Repository Of Well-resolved Noncovalent interactions

The development of machine-learning models for protein-ligand interactions is constrained by the quality and diversity of the available structural data. Existing resources force researchers into a trade-off: carefully curated collections such as PDBBind and HiQBind offer high structural reliability but cover only a narrow slice of the Protein Data Bank (PDB), whereas large-scale resources such as PLINDER provide broad coverage with minimal quality control. We present CROWN (Curated Repository Of Well-resolved Non-covalent interactions), a machine-learning-ready dataset that reconciles scale and rigor through a fully automated preprocessing pipeline. Starting from the PDB database, CROWN applies a series of interleaved quality filters and processing stages that address crystallographic resolution, ligand identity, pocket completeness, structural repair, interaction quality, and protonation at physiological pH. The pipeline finishes with a constrained energy-minimization step built on custom flat-bottomed restraints - a step absent from all existing protein-ligand datasets - that balances crystallographic evidence against the relaxation of intramolecular strain. By reconciling the heterogeneous refinement practices of different depositions without distorting the experimentally observed binding geometry, this step yields a structurally uniform collection of 178,263 complexes, representing a roughly four-fold increase in protein diversity over PDBBind and HiQBind. Rather than organizing the data around sparsely available, bias-prone binding affinities, CROWN adopts a geometry-centric design philosophy that treats the three-dimensional arrangement of atoms at the binding interface as a self-consistent source of information. To demonstrate its value as a training resource, we trained two knowledge-based scoring functions on CROWN and benchmarked them on CASF-2016: relative to HiQBind-trained counterparts, CROWN-trained models showed markedly improved ranking power (mean Spearman correlation rising from 0.509 to 0.637) and docking power (top-1 near-native pose recovery of 0.785 versus 0.724). Because CROWN imposes no requirement for affinity labels, it can in principle support any model that learns from or is evaluated against protein-ligand complex structures. We anticipate that it will serve as a broadly useful resource for tasks such as the training of binder generation, protein design or protein folding models conditioned on bound ligands, the development of scoring functions or benchmarking of interaction-prediction methods.

bioinformatics↗

bioRxiv2022

Antibody-lectin chimeras for glyco-immune checkpoint blockade

Despite the curative potential of checkpoint blockade immunotherapy, most patients remain unresponsive to existing treatments. Glyco-immune checkpoints - interactions of cell-surface glycans with lectin, or glycan-binding, immunoreceptors - have emerged as prominent mechanisms of immune evasion and therapeutic resistance in cancer. Here, we describe antibody-lectin chimeras (AbLecs), a modular platform for glyco-immune checkpoint blockade. AbLecs are bispecific antibody-like molecules comprising a cell-targeting antibody domain and a lectin "decoy receptor" domain that directly binds glycans and blocks their ability to engage inhibitory lectin receptors. AbLecs potentiate anticancer immune responses including phagocytosis and cytotoxicity, outperforming most existing therapies and combinations tested. By targeting a distinct axis of immunological regulation, AbLecs synergize with blockade of established immune checkpoints. AbLecs can be readily designed to target numerous tumor and immune cell subsets as well as glyco-immune checkpoints, and therefore represent a new modality for cancer immunotherapy.

biochemistry↗

bioRxiv2021

MVsim: a toolset for quantifying and designing multivalent interactions

Arising through multiple binding elements, multivalency can specify the avidity, duration, cooperativity, and selectivity of biomolecular interactions, but quantitative prediction and design of these properties has remained challenging. Here we present MVsim, an application suite built around a configurational network model of multivalency to facilitate the quantification, design, and mechanistic evaluation of multivalent binding phenomena through a simple graphical user interface. To demonstrate the utility and versatility of MVsim, we first show that both monospecific and multispecific multivalent ligand-receptor interactions, with their noncanonical binding kinetics, can be accurately simulated. We then quantitatively predict the ultrasensitivity and performance of multivalent-encoded protein logic gates, evaluate the inherent programmability of multispecificity for selective receptor targeting, and extract rate constants of conformational switching for the SARS-CoV-2 spike protein and model its binding to ACE2 as well as multivalent inhibitors of this interaction. MVsim is freely available at https://sarkarlab.github.io/MVsim/.

systems biology↗

bioRxiv2020

Educational attainment polygenic scores in Hungary: evidence for validity and a historical gene-environment interaction

Educational attainment is a substantially heritable trait, and it has recently been linked to specific genetic variants by genome-wide association studies (GWASs). However, the effects of such genetic variants are expected to vary across environments, including countries and historical eras. We used polygenic scores (PGSs) to assess molecular genetic effects on educational attainment in Hungary, a country in the Central Eastern European region where behavioral genetic studies are in general scarce and molecular genetic studies of educational attainment have not been previously published. We found that the PGS is significantly associated with highest educational level attained as well as the number of years in education in a sample of Hungarian volunteers (N=829). In an English (N=976) comparison sample with identical measurement protocols the same PGS had a stronger association with educational level, but not with years in education. In line with previous Estonian findings, we found higher PGS effect sizes in Hungarian, but not in English participants who attended higher education after the fall of Communism, although we lacked statistical power for this effect to reach significance. Our results provide evidence that polygenic scores for educational attainment are valid in diverse European populations.

genetics↗