Biology subjects

Kitaygorodsky, A.

Publications and source records attributed to Kitaygorodsky, A..

4 recordsLinked to original sources

bioRxiv2026

AtomWeaver: Multi-Component Flow Matching with a Structured Geometric Prior Facilitates Non-Canonical Peptide Design

Fixed-backbone sequence discovery, or inverse folding, is a critical recurring task in the development of new polypeptide therapeutics. Once promising backbones are established for a target pocket, computational inverse folding methods greatly help accelerate generation of candidate sequences. Such methods are mature for the traditional case of limiting to the fixed twenty-letter canonical vocabulary; however, they cannot access the broader space of non-canonical amino acids (NCAAs). This design constraint is exacerbated for peptide binders, a fast-growing modality that readily incorporates NCAAs, though in practice non-canonical design frequently depends on laborious medicinal-chemistry campaigns. An extension of inverse folding to NCAAs is thus critical to accelerating design of novel therapeutic peptides. AtomWeaver uses a joint all-site, atom-level generative scheme that does not restrict side-chain categorical assignment by either predetermined or co-resolving residue identity. Conditioned only on a fixed peptide backbone and its target protein, its multi-component flow guides side-chain atoms as unlabeled points in R3 from a nested shell prior to a variable-count final atom cloud. Identity is then read by matching each predicted cloud against a reference library of canonical and non-canonical templates. Since identity is decided only at decode time, the addressable vocabulary is a property of the library rather than of the trained weights: a new NCAA costs one reference structure and no retraining, and the model can select residues it was never prompted for and never saw in training. On a deep mutational scan of two peptide-target systems, AtomWeaver's canonical readout shows high observed mean agreement with experimental values among the compared inverse-folding methods. In the mixed canonical-noncanonical setting that canonical-only baselines cannot support at all, it likewise retains ranking signal across both systems. AtomWeaver also displayed self-consistent designs on de novo binder backbones, with the highest interface confidence among compared methods. Notably, it reached these metrics while achieving broad empirical coverage of our 300-residue vocabulary, including four non-canonical types never visible in training. AtomWeaver thus serves canonical and non-canonical peptide design alike, while transforming residue vocabulary to an expandable inference-time choice.

bioengineering↗

bioRxiv2025

Improved De Novo Peptide Binder Design with Target-Conditioned Inverse Folding

Inverse protein folding methods have become central to the computational design of de novo proteins, but existing models struggle when tasked with generating high-affinity peptide binders. By combining peptide-specific finetuning with a novel decoding order strategy, we enhance pocket conditioning and enable more accurate sequence design for peptide-binding interfaces. Our approach delivers gains in computational metrics, increasing sequence recovery and improving in silico binder design success rate by 16% 30%. In vitro validation finds that our method greatly improves the success rate of designing novel peptide agonists of the OPRM1 receptor, generating at least twice as many top-ranking agonists as the prevailing standard method ProteinMPNN.

bioengineering↗

bioRxiv2021

Predicting localized affinity of RNA binding proteins to transcripts with convolutional neural networks

RNA binding proteins (RBPs) are important regulators of transcriptional and post-transcriptional processes. Computational prediction of localized RBP binding affinity with transcripts is important for interpretation of genetic variation, especially variants outside of protein coding region. Here we describe POLARIS (Prediction Of Localized Affinity for RBPs In Sequence), a new deep-learning method for achieving fast, site-specific binding affinity predictions of RNA-binding proteins (RBPs) to the transcribed genome. POLARIS has two modules: 1. a convolutional neural network (CNN) to predict overall RBP binding within a region based on transcript sequence content and expression level; 2. a Gradient-weighted Class Activation Mapping (GradCAM) implementation for efficient signal backpropagation to individual sequence positions. We trained the model using enhanced crosslinking and immunoprecipitation (eCLIP) data from ENCODE. POLARIS has good performance with a median AUC ~ 0.96 for 160 RBPs across three different cell lines, substantially higher than selected popular published methods trained and tested on the same data sets. When tested on data from a different cell line with the same RBPs, the overall performance is maintained, supporting the ability of cell-type specific affinity prediction. Finally, the GradCAM module allows the model to identify the informative sites in a region that drive prediction. The localized prediction facilitates interpretation of the results and provides basis for inference of functional impact of noncoding variants.

bioinformatics↗

bioRxiv2017

Genetic analysis of de novo variants reveals sex differences in complex and isolated congenital diaphragmatic hernia and indicates MYRF as a candidate gene

Congenital diaphragmatic hernia (CDH) is one of the most common and lethal birth defects. Previous studies using exome sequencing support a significant contribution of coding de novo variants in complex CDH cases with additional anomalies and likely gene-disrupting (LGD) variants in isolated CDH cases. To further investigate the genetic architecture of CDH, we performed exome or genome sequencing in 283 proband-parent trios. Combined with data from previous studies, we analyzed a total of 357 trios, including 148 complex and 209 isolated cases. Complex and isolated cases both have a significant burden of deleterious de novo coding variants (1.7~fold, p= 1.2x10-5 for complex, 1.5~fold, p= 9.0x10-5 for isolated). Strikingly, in isolated CDH, almost all of the burden is carried by female cases (2.1~fold, p=0.004 for likely gene disrupting and 1.8~fold, p= 0.0008 for damaging missense variants); whereas in complex CDH, the burden is similar in females and males. Additionally, de novo LGD variants in complex cases are mostly enriched in genes highly expressed in developing diaphragm, but distributed in genes with a broad range of expression levels in isolated cases. Finally, we identified a new candidate risk gene MYRF (4 de novo variants, p-value=2x10-10), a transcription factor intolerant of mutations. Patients with MYRF mutations have additional anomalies including congenital heart disease and genitourinary defects, likely representing a novel syndrome.

genomics↗