Biology subjects

Kohli, P.

Publications and source records attributed to Kohli, P..

9 recordsLinked to original sources

bioRxiv2026

Designing enzymes for new-to-nature chemistry and non-natural substrates with AlphaProtein Novo

Creating highly active enzymes for arbitrary reactions is a transformative goal for the molecular sciences (1). Traditional protein engineering (2,3) is constrained by a reliance on existing natural starting points, which are often challenging to discover. De novo design offers the potential to overcome this by creating enzymes from first principles (4), but has yet to achieve practically relevant catalytic properties. Here we present AlphaProtein Novo (AP Novo), a machine-learning pipeline that demonstrates, for the first time, that de novo enzyme design can outperform natural sequence mining in addressing challenging chemistry. We used AP Novo to design new-to-nature nitrene transferases to synthesise the pharmacophore piperidine with unprecedented product selectivity, and to create enzymes that degrade the environmental toxin DEHP under conditions that denature natural enzymes. We also obtained state-of-the-art catalytic efficiencies on two well-studied model reactions, and through iterative design and analysis found that key enablers of success were mechanism-inspired metrics based on AlphaFold 3 predictions and sequence-ensemble-based scoring of designed backbones. Our results show that de novo design has become a powerful complement to natural enzyme diversity for discovering catalysts for a range of applications.

synthetic biology↗

bioRxiv2026

Design-Evolution Synergy Enables New-to-Nature Enzyme Chemistry

Directed evolution and machine-learning-assisted evolution have transformed biocatalysis by enabling rapid optimisation of catalytic performance within compatible protein scaffolds. In parallel, advances in deep-learning-driven de novo protein design have expanded accessible protein architectural space, enabling the generation of functional catalysts for increasingly challenging transformations. We herein report a synergistic design-evolution strategy to encode new-to-nature regio- and enantioselective C-H amination reactions using engineered de novo haemproteins to synthesise chiral piperidines, a privileged pharmacophore in drug discovery. By integrating motif scaffolding, computational re-design, and directed evolution, we demonstrate de novo enzyme design can enable challenging, high-energy-barrier enzyme chemistry with programmable regio- and enantioselectivity, high catalytic efficiency, excellent enzyme stability, and scaffold diversity.

synthetic biology↗

bioRxiv2025

Exploring the Microbial Geobiological Pattern Across the Serpentinization Sites through Metagenomic and Elemental Composition Analyses

It is well-known that ultramafic rocks can continuously generate hydrogen through the serpentinization process associated with dynamic geochemical and geobiological interactions. This study aims to gain an improved understanding of these dynamic processes through the understanding of diverse microbial populations in order to maximize the geologic hydrogen production potential. The water samples were collected from a near surface serpentinite site in Northern California, USA. Elemental analyses of these water samples revealed the presence of essential dissolved minerals such as magnesium, calcium, and potassium, along with trace elements including iron, cobalt, and nickel. These elements act as cofactors for enzymes involved in microbial metabolic processes and Non-metric Multidimensional Scaling analysis supports this corelation. Furthermore, the analysis of total organic carbon (TOC) showed significant levels of organic carbon, suggesting a link with biological carbon cycling processes. Metagenomic analysis uncovered a diverse microbial community of hydrogen-fueled microbial consortia at the sampling sites, encompassing hydrogenogenic and hydrogenotrophic microorganisms. We examine hydrogen-metabolizing communities, including sulfate-reducing bacteria, acetogens, and methanogens, supported by diversity in hydrogenase enzymes across various sampling sites. These observations are corroborated by genomic accession data and abundance profiling of genes associated with acetogenesis, methanogenesis, and carbon monoxide metabolism. These investigations provide new insights into hydrogen-metabolizing microorganisms in northern California and propose frameworks for optimizing hydrogen production through the inhibition of hydrogen metabolism. O_FIG O_LINKSMALLFIG WIDTH=155 HEIGHT=200 SRC="FIGDIR/small/678891v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@11e5041org.highwire.dtl.DTLVardef@126ea56org.highwire.dtl.DTLVardef@1e2cd4forg.highwire.dtl.DTLVardef@11537f2_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIThe association of the serpentinization process with dynamic geochemical and geobiological interactions. C_LIO_LIMetagenomic study found an assorted microbial community of hydrogen-fueled consortia at the different sampling sites in Northern California C_LIO_LIHydrogen-metabolizing communities counted, including sulfate-reducing bacteria, acetogens, and methanogens. C_LI

microbiology↗

bioRxiv2025

AlphaGenome: advancing regulatory variant effect prediction with a unified DNA sequence model

Deep learning models that predict functional genomic measurements from DNA sequence are powerful tools for deciphering the genetic regulatory code. Existing methods trade off between input sequence length and prediction resolution, thereby limiting their modality scope and performance. We present AlphaGenome, which takes as input 1 megabase of DNA sequence and predicts thousands of functional genomic tracks up to single base pair resolution across diverse modalities - including gene expression, transcription initiation, chromatin accessibility, histone modifications, transcription factor binding, chro- matin contact maps, splice site usage, and splice junction coordinates and strength. Trained on human and mouse genomes, AlphaGenome matches or exceeds the strongest respective available external models on 24 out of 26 evaluations on variant effect prediction. AlphaGenomes ability to simultaneously score variant effects across all modalities accurately recapitulates the mechanisms of clinically-relevant variants near the TAL1 oncogene. To facilitate broader use, we provide tools for making genome track and variant effect predictions from sequence.

bioinformatics↗

bioRxiv2024

CcdA chaperones CcdB against irreversible misfolding and aggregation via a cotranslational folding mechanism

Cotranslational subunit assembly is thought to be a prominent feature throughout the proteome, but in bacteria, there are only a limited number of experimentally confirmed examples and most involve addition of extraneous tag sequences for experimental convenience. CcdA and CcdB are the antitoxin and toxin components, respectively, of the ccdAB operon. They assemble in a hetero-multimeric complex in vivo. Mutant phenotypes in a saturation mutagenesis library of CcdB were inferred from deep sequencing in two contexts, one when CcdB is expressed alone, and the other when CcdB is present in an operonic context downstream of its cognate antitoxin, CcdA. When expressed in the absence of CcdA, charged and polar mutations in the CcdB core cause the protein to misfold. However, many such deleterious mutations are rescued when expressed along with CcdA in the native operon. CcdA thus acts as a chaperone and relieves the folding defect in CcdBcotranslationally. Assembly efficiency and efficacy decreases when CcdA and CcdB are expressed from separate mRNAs relative to when they are expressed from the same polycistronic mRNA. Gene organisation in operons in bacteria may thus reflect a fundamental cotranslational mechanism that is important for effective assembly of protein complexes and can potentially buffer substantial genetic variation. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=161 SRC="FIGDIR/small/618527v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@26e1ceorg.highwire.dtl.DTLVardef@b2a4b1org.highwire.dtl.DTLVardef@5a7c85org.highwire.dtl.DTLVardef@a50512_HPS_FORMAT_FIGEXP M_FIG C_FIG In BriefUsing the CcdAB Type II Toxin-Antitoxin system, the authors show that the antitoxin is able to suppress folding defects of the toxin, likely by processes involving cotranslational assembly. HighlightsO_LIDeep mutational scanning is used to assess and compare the foldability of buried-site CcdB mutants in the absence and presence of CcdA. C_LIO_LIFolding defects of CcdB mutants are rescued in the presence of CcdA when expressed in an operonic context. C_LIO_LICcdAB complex formation is enhanced when both the toxin and antitoxin components are synthesized from the same polycistronic mRNA, relative to synthesis from separate mRNAs. C_LIO_LICcdA acts as chaperone both to assist correct folding, and to buffer mutations in CcdB, likely via cotranslational assembly mechanisms. C_LI

biophysics↗

bioRxiv2022

Codon optimality is the primary contributor to the exceptional mutational sensitivity of CcdA antitoxin in its operonic context

Deep mutational scanning studies suggest that synonymous mutations are typically silent and that most exposed, non active-site residues are tolerant to mutations. Here we show that the ccdA antitoxin component of the E.coli ccdAB toxin-antitoxin system is unusually sensitive to mutations when studied in the operonic context. A large fraction ([~]80%) of single-codon mutations, including many synonymous mutations in the ccdA gene shows inactive phenotype, but they retain native-like binding affinity towards cognate toxin, CcdB. Therefore, the observed phenotypic effects are largely not due to alterations in protein structure/stability, consistent with a large region of CcdA being intrinsically disordered. E. coli codon preference and strength of ribosome-binding associated with translation of downstream ccdB gene are found to be major contributors of the observed mutant phenotypes. In select cases, proteomics studies reveal altered ratios of CcdA:CcdB protein levels in vivo, suggesting that the ccdA mutations likely alter relative translation efficiencies of the two genes in the operon. We extend these results by studying single-site synonymous mutations that lead to loss of function phenotypes in the relBE operon upon introduction of rarer codons. Thus, in their operonic context, genes are likely to be more sensitive to both synonymous and non-synonymous point mutations than inferred previously.

molecular biology↗

bioRxiv2021

Protein complex prediction with AlphaFold-Multimer

While the vast majority of well-structured single protein chains can now be predicted to high accuracy due to the recent AlphaFold [1] model, the prediction of multi-chain protein complexes remains a challenge in many cases. In this work, we demonstrate that an AlphaFold model trained specifically for multimeric inputs of known stoichiometry, which we call AlphaFold-Multimer, significantly increases accuracy of predicted multimeric interfaces over input-adapted single-chain AlphaFold while maintaining high intra-chain accuracy. On a benchmark dataset of 17 heterodimer proteins without templates (introduced in [2]) we achieve at least medium accuracy (DockQ [3] [≥] 0.49) on 13 targets and high accuracy (DockQ [≥] 0.8) on 7 targets, compared to 9 targets of at least medium accuracy and 4 of high accuracy for the previous state of the art system (an AlphaFold-based system from [2]). We also predict structures for a large dataset of 4,446 recent protein complexes, from which we score all non-redundant interfaces with low template identity. For heteromeric interfaces we successfully predict the interface (DockQ [≥] 0.23) in 70% of cases, and produce high accuracy predictions (DockQ [≥] 0.8) in 26% of cases, an improvement of +27 and +14 percentage points over the flexible linker modification of AlphaFold [4] respectively. For homomeric inter-faces we successfully predict the interface in 72% of cases, and produce high accuracy predictions in 36% of cases, an improvement of +8 and +7 percentage points respectively.

bioinformatics↗

bioRxiv2021

Effective gene expression prediction from sequence by integrating long-range interactions

The next phase of genome biology research requires understanding how DNA sequence encodes phenotypes, from the molecular to organismal levels. How noncoding DNA determines gene expression in different cell types is a major unsolved problem, and critical downstream applications in human genetics depend on improved solutions. Here, we report substantially improved gene expression prediction accuracy from DNA sequence through the use of a new deep learning architecture called Enformer that is able to integrate long-range interactions (up to 100 kb away) in the genome. This improvement yielded more accurate variant effect predictions on gene expression for both natural genetic variants and saturation mutagenesis measured by massively parallel reporter assays. Notably, Enformer outperformed the best team on the critical assessment of genome interpretation (CAGI5) challenge for noncoding variant interpretation with no additional training. Furthermore, Enformer learned to predict promoter-enhancer interactions directly from DNA sequence competitively with methods that take direct experimental data as input. We expect that these advances will enable more effective fine-mapping of growing human disease associations to cell-type-specific gene regulatory mechanisms and provide a framework to interpret cis-regulatory evolution. To foster these downstream applications, we have made the pre-trained Enformer model openly available, and provide pre-computed effect predictions for all common variants in the 1000 Genomes dataset. One-sentence summaryImproved noncoding variant effect prediction and candidate enhancer prioritization from a more accurate sequence to expression model driven by extended long-range interaction modelling.

genomics↗

bioRxiv2019

Loss of function from widely distributed, synonymous mutations at single codons

Mutational tolerance inferred from laboratory-based mutational studies is typically much higher than observed natural sequence variation. Using saturation mutagenesis, we show that the ccdA antitoxin component of the ccdAB toxin-antitoxin system is unusually sensitive to mutation with over 60% of mutations leading to loss of function. Multi-base synonymous mutations at a codon display enhanced propensity to show altered phenotypes, relative to single-base ones. Such mutations modulate RNA structure, leading to altered relative translation efficiencies of the two genes in the operon, and a CcdA:CcdB protein ratio below one. These insights were used to predict and experimentally validate synonymous mutations that lead to loss of function in the unrelated relBE operon as well as the lacZ gene. Thus, synonymous mutations can have significant phenotypic effects, in the absence of overexpression or extraneous reporters. More generally, proteins are likely more sensitive to mutation than inferred from previous saturation mutagenesis studies.

molecular biology↗