Biology subjects

Lipsh-Sokolik, R.

Publications and source records attributed to Lipsh-Sokolik, R..

4 recordsLinked to original sources

bioRxiv2026

Mapping and Probing the Substrate Scope of a Ketoamide-Forming Splicease by Computational Design

-Keto-{beta}-amide warheads are protease-inhibiting pharmacophores in peptidomimetic antiviral drugs and drug leads, but their diversity-oriented synthesis for library-based drug discovery can be challenging. The same moiety is installed enzymatically in gene-encoded peptides by tyramine spliceases from spliceotide natural products. However, their substrate scope remains poorly understood. We mined bacterial spliceotide pathways for multi-precursor spliceases and identified HypX from Hyella patelloides that modified 5 of 7 native precursors in E. coli. Single-point mutagenesis showed promiscuity for HypX and the previously identified PlpX but different substrate scopes, suggesting that splicease mining can provide enzymes for focused libraries of various substrate types. To evaluate multipoint variants without exhaustive screening, we combined AlphaFold3 models with Rosetta calculations to rank millions of variants. Top-scoring peptides were spliced more efficiently than single-point mutants. Structural modeling rationalized position-specific tolerance, including a cofactor constraint. This work establishes a mining- and structure-guided strategy to streamline library-based splicease applications.

synthetic biology↗

bioRxiv2022

Designed active-site library reveals thousands of functional GFP variants

Mutations in a protein active site can lead to dramatic and useful changes in protein activity. The active site, however, is extremely sensitive to mutations due to a high density of molecular interactions, drastically reducing the likelihood of obtaining functional multipoint mutants. We introduce an atomistic and machine-learning-based approach, called htFuncLib, to design a sequence space in which mutations form low-energy combinations that mitigate the risk of incompatible interactions. We applied htFuncLib to the GFP chromophore-binding pocket, and, using fluorescence readout, recovered >16,000 unique designs encoding as many as eight active-site mutations. Many designs exhibit substantial and useful diversity in functional thermostability (up to 96 {degrees}C), fluorescence lifetime, and quantum yield. By eliminating incompatible active-site mutations, htFuncLib generates a large diversity of functional sequences. We envision that htFuncLib will be useful for one-shot optimization of activity in enzymes, binders, and other proteins.

biochemistry↗

bioRxiv2022

Modularly designed protein fragments combine into thousands of active and structurally diverse enzymes

Design of structurally diverse enzymes is constrained by long-range interactions that are needed for accurate folding. We introduce an atomistic and machine-learning strategy for Combinatorial Assembly and Design of ENZymes, CADENZ, to design fragments that combine with one another to generate diverse, low-energy structures with stable catalytic constellations. We applied CADENZ to endoxylanases and used activity-based protein profiling to recover thousands of active and structurally diverse enzymes. Functional designs exhibit high active-site preorganization and more stable and compact packing outside the active site. Implementing these lessons into CADENZ led to a tenfold improved hit rate and >10,000 active enzymes. This design-test-learn loop can be applied, in principle, to any modular protein family, yielding huge diversity and general lessons on protein design principles.

bioengineering↗

bioRxiv2021

Assessing and enhancing foldability in designed proteins

Recent advances in protein-design methodology have led to a dramatic increase in reliability and scale. With these advances, dozens and even thousands of designed proteins are automatically generated and screened. Nevertheless, the success rate, particularly in design of functional proteins, is low and fundamental goals such as reliable de novo design of efficient enzymes remain beyond reach. Experimental analyses have consistently indicated that a major reason for design failure is inaccuracy and misfolding relative to the design model. To address this challenge, we describe complementary methods to diagnose and ameliorate suboptimal regions in designed proteins: first, we develop a Rosetta atomistic computational mutation scanning approach to detect energetically suboptimal positions in designs (available on a web server https://pSUFER.weizmann.ac.il); second, we demonstrate that AlphaFold2 ab initio structure prediction flags regions that may misfold in designed enzymes and binders; and third, we focus FuncLib design calculations on suboptimal positions in a previously designed low-efficiency enzyme improving its catalytic efficiency by 330 fold. Furthermore, applied to a de novo designed protein that exhibited limited stability, the same approach markedly improved stability and expressibility. Thus, foldability analysis and enhancement may dramatically increase the success rate in design of functional proteins.

biochemistry↗