Biology subjects

del Alamo, D.

Publications and source records attributed to del Alamo, D..

10 recordsLinked to original sources

bioRxiv2026

Structure-based Antibody Renumbering

Antibody numbering schemes like IMGT and Chothia assign each residue in the variable domain a consistent index based on substructural position. These annotations standardize sequences with different lengths, facilitating tasks ranging from engineering of individual molecules during drug development to large-scale curation of training data for de novo antibody design. Yet existing algorithms for performing this annotation process, termed renumbering, rely exclusively on amino acid sequence for inference. Consequently, these can fail when presented with unnatural or unusual features such as long CDRs or engineered insertions. To address this gap, this work introduces Structure-based Antibody Renumbering, abbreviated SAbR, a method that assigns these annotations from structure alone. SAbR shows comparable performance to sequence-based renumbering methods on held-out expert-annotated structures, as well as high agreement with sequence-based methods in a larger benchmark of diverse structures. It also outperforms peer methods on de novo-designed molecules, and shows higher success rates than renumbering by structural alignment. However, limited generalization performance is observed in more distantly related systems. Overall, these results establish structure-based renumbering as a robust alternative for natural and engineered antibodies when such data is available. Code and model weights are available on GitHub.

bioinformatics↗

bioRxiv2025

Adapting ProteinMPNN for antibody design without retraining

The neural network ProteinMPNN designs protein sequences capable of folding into predefined tertiary structures and quaternary assemblies. It has become widely used due to its high success rates when working with synthetic topologies rich in secondary structure. Here, we show degraded performance on the complementarity-determining regions (CDRs) of antibodies, with designs frequently failing to resemble native antibodies or failing to refold into the designed conformations. We also show that this underperformance can be rescued by ensembling its predictions with those from the antibody-specific protein language model AbLang, which designs exclusively using sequence information learned from large databases of antibody sequences. Finally, we tested 96 trastuzumab variants with CDRH3 loops redesigned by the ensembled ProteinMPNN+AbLang method and found that it generated thirty-six HER2 binders, compared to three out of 96 designs generated by ProteinMPNN alone. The data highlight the value of incorporating additional restraints derived from language models during structure-based sequence design of antibodies.

bioengineering↗

bioRxiv2023

Conformational sampling and interpolation using language-based protein folding neural networks

Protein language models (PLMs), such ESM2, learn a rich semantic grammar of the protein sequence space. When coupled to protein folding neural networks (e.g., ESMFold), they can facilitate the prediction of tertiary and quaternary protein structures at high accuracy. However, they are limited to modeling protein structures in single states. This manuscript demonstrates that ESMFold can predict alternate conformations of some proteins, including de novo designed proteins. Randomly masking the sequence prior to PLM input returned alternate embeddings that ESMFold sometimes mapped to distinct physiologically relevant conformations. From there, inversion of the ESMFold trunk facilitated the generation of high-confidence interconversion paths between the two states. These paths provide a deeper glimpse of how language-based protein folding neural networks derive structural information from high-dimensional sequence representations, while exposing limitations in their general understanding of protein structure and folding.

biophysics↗

bioRxiv2023

ESMFold Hallucinates Native-Like Protein Sequences

We describe attempts to design protein sequences by inverting the protein structure prediction algorithm ESMFold. State-of-the-art protein structure prediction methods achieve high accuracy by relying on evolutionary patterns derived from either multiple sequence alignments (AlphaFold, RosettaFold) or pretrained protein language models (PLMs; ESMFold, OmegaFold). In principle, by inverting these networks, protein sequences can be designed to fulfill one or more design objectives, such as high prediction confidence, predicted protein binding, or other geometric constraints that can be expressed with loss functions. In practice, sequences designed using an inverted AlphaFold model, termed AFDesign, contain unnatural sequence profiles shown to express poorly, whereas an inverted RosettaFold network has been shown to be sensitive to adversarial sequences. Here, we demonstrate that these limitations do not extend to neural networks that include PLMs, such as ESMFold. Using an inverted ESMFold model, termed ESM-Design, we generated sequences with profiles that are both more native-like and more likely to express than sequences generated using AFDesign, but less likely to express than sequences rescued by the structure-based design method ProteinMPNN. However, the safeguard offered by the PLM came with steep increases in memory consumption, preventing proteins greater than 150 residues from being modeled on a single GPU with 80GB VRAM. During this investigation, we also observed the role played by different sequence initialization schemes, with random sampling of discrete amino acids improving convergence and model quality over any continuous random initialization method. Finally, we showed how this approach can be used to introduce sequence and structure diversification in small proteins such as ubiquitin, while respecting the sequence conservation of active site residues. Our results highlight the effects of architectural differences between structure prediction networks on zero-shot protein design.

bioinformatics↗

bioRxiv2022

Integrated AlphaFold2 and DEER investigation of the conformational dynamics of a pH-dependent APC antiporter

The Amino Acid-Polyamine-Organocation transporter GadC contributes to the survival of pathogenic bacteria under extreme acid stress by exchanging extracellular glutamate for intracellular GABA. Its structure, determined exclusively in an inward-facing conformation at alkaline pH, consists of the canonical LeuT-fold of a conserved five-helix inverted repeat, thereby resembling functionally divergent transporters such as the serotonin reuptake transporter SERT and the glucose-sodium symporter transporter SGLT1. However, despite this structural similarity, it is unclear if the conformational dynamics of antiporters such as GadC follows the blueprint of these or other well-studied LeuT-fold transporters. Here, we used double electron-electron resonance (DEER) spectroscopy to monitor the conformational dynamics of GadC in lipid bilayers in response to acidification and substrate binding. To guide experimental design and facilitate the interpretation of the DEER data, we generated an ensemble of structural models in multiple conformations using a recently introduced AlphaFold2 methodology. Our experimental results reveal acid-induced conformational changes that dislodge the C-terminus from the permeation pathway coupled with rearrangement of helices that enable isomerization between both inward- and outward-facing states. The substrate glutamate, but not GABA, modulates the dynamics of an extracellular thin gate without shifting the equilibrium between inward- and outward-facing conformations. In addition to introducing an integrated methodology for probing transporter conformational dynamics, the congruence of the DEER data with patterns of structural rearrangements deduced from ensembles of AlphaFold2 models illuminate the conformational cycle of GadC underpinning transport and exposes yet another example of the divergence between the dynamics of different functional families in the LeuT-fold. SIGNIFICANCE STATEMENTThe transporter GadC contributes to acid resistance in bacterial pathogens by exchanging two substrates, glutamate and GABA, using a mechanism termed alternating access. In this study, the conformational dynamics underlying alternating access was studied using a combination of spectroscopy and computational modeling. A conformationally diverse ensemble of models, generated using AlphaFold2, guided the design and interpretation of double electron-electron resonance spectroscopy experiments. We found that whereas GadC was inactive and conformationally homogeneous at neutral pH, low pH induced isomerization between two conformations. From our integrated computational/experimental investigation emerges a transport model that may be relevant to eukaryotic homologs that are involved in other cellular processes.

biophysics↗

bioRxiv2022

Modeling of protein conformational changes with Rosetta guided by limited experimental data

Conformational changes are an essential component of functional cycles of many proteins such as transporters or receptors. Despite their importance, the characterization of functionally-relevant structural intermediates is often a hard task for both experimental and computational methods. This limitation can be at least partially relieved by integrating experimental data within computational approaches. Such integrative structural biology strategies can yield an ensemble of models consistent with experimental data. While the macromolecular modeling suite Rosetta has been extensively used to predict protein structures de novo using various types of experimental data, an all-purpose conformational change modeling approach has been absent from this suite. Here, we introduce and benchmark ConfChangeMover (CCM), a new Rosetta Mover tailored to model conformational changes in proteins using sparse experimental data. CCM is able to rotate and translate secondary structural elements and modify their backbone dihedral angles in regions of interest. We benchmarked CCM on soluble and membrane proteins with sparse experimental double electron-electron resonance (DEER) restraints and simulated C - C distance restraints, respectively. In both benchmarks, CCM outperformed state-of-the-art Rosetta methods, showing that it can model a diverse array of conformational changes. In addition, the Rosetta framework allows a wide variety of experimental data to be integrated with CCM, thus extending its capability beyond DEER restraints. Thus, our method will contribute to the biophysical characterization of protein dynamics.

biophysics↗

bioRxiv2021

Sampling the conformational landscapes of transporters and receptors with AlphaFold2

Equilibrium fluctuations and triggered conformational changes often underlie the functional cycles of membrane proteins. For example, transporters mediate the passage of molecules across cell membranes by alternating between inward-facing (IF) and outward-facing (OF) states, while receptors undergo intracellular structural rearrangements that initiate signaling cascades. Although the conformational plasticity of these proteins has historically posed a challenge for traditional de novo protein structure prediction pipelines, the recent success of AlphaFold2 (AF2) in CASP14 culminated in the modeling of a transporter in multiple conformations to high accuracy. Given that AF2 was designed to predict static structures of proteins, it remains unclear if this result represents an underexplored capability to accurately predict multiple conformations and/or structural heterogeneity. Here, we present an approach to drive AF2 to sample alternative conformations of topologically diverse transporters and G-protein coupled receptors (GPCRs) that are absent from the AF2 training set. Whereas models generated using the default AF2 pipeline are conformationally homogeneous and nearly identical to one another, reducing the depth of the input multiple sequence alignments (MSAs) by stochastic subsampling led to the generation of accurate models in multiple conformations. In our benchmark, these conformations spanned the range between two experimental structures of interest, with models at the extremes of these conformational distributions observed to be among the most accurate (average template modeling (TM)-score of 0.94). These results suggest a straightforward approach to identifying native-like alternative states, while also highlighting the need for the next generation of deep learning algorithms to be designed to predict ensembles of biophysically relevant states.

biophysics↗

bioRxiv2019

Regulation of Numb during planar cell polarity establishment in the Drosophila eye

The establishment of planar cell polarity (PCP) in the Drosophila eye requires correct specification of the R3/R4 pair of photoreceptor cells, determined by a Frizzled mediated signaling event that specifies R3 and induces Delta to activate Notch signaling in the neighboring cell, specifying it as R4. Here, we investigated the role of the Notch signaling negative regulator Numb in the specification of R3/R4 fates and PCP establishment in the Drosophila eye. We observed that Numb is transiently upregulated in R3 at the time of R3/R4 specification. This regulation of Numb levels in developing photoreceptors occurs at the post-transcriptional level and is dependent on Dishevelled, an effector of Frizzled signaling, and Lethal Giant Larva. We detected PCP defects in cells homozygous for numb15, but these defects were due to a loss of function mutation in fat (fatQ805*) being present in the numb15 chromosome. However, mosaic overexpression of Numb in R4 precursors (only) caused PCP defects and numb loss-of-function had a modifying effect on the defects found in a hypomorphic dishevelled mutation. Our results suggest that Numb levels are upregulated to reinforce the bias of Notch signaling activation in the R3/R4 pair, two post-mitotic cells that are not specified by asymmetric cell division.

developmental biology↗

bioRxiv2019

Protein fold prediction using simulated DEER distance distributions and decay traces

Despite advances in sampling and scoring strategies, Monte Carlo modeling methods still struggle to accurately predict de novo the structures of large proteins, membrane proteins, or proteins of complex topologies. Previous approaches have addressed these shortcomings by leveraging sparse distance data gathered using site-directed spin labeling and electron paramagnetic resonance spectroscopy (SDSL-EPR) to improve protein structure prediction and refinement outcomes. However, existing computational implementations must choose between coarse-grained models of the spin label that lower the resolution and explicit models that lead to resource-intense simulations. Existing methods are further limited by their reliance on distance distributions, which are calculated from a primary refocused echo decay signal and may contain artifacts introduced during this processing step. Here, we addressed these challenges by developing RosettaDEER, a scoring method within the Rosetta software suite capable of simulating distance distributions and echo decay traces between spin labels fast enough to fold proteins de novo. We demonstrate that the accuracy of resulting distance distributions match or exceed those generated by more computationally intensive methods. Moreover, decay traces generated from these distributions recapitulate intermolecular background coupling parameters, allowing RosettaDEER to discriminate between poorly-folded and native-like models even when the time window of EPR data collection is truncated, rendering them unsuitable for accurate transformation into distance distributions. Finally, we demonstrate that one decay trace per nine residues is sufficient to predict the folds of Bax and the C-terminus of ExoU, two soluble proteins with surface-exposed amphipathic structural features that prevent the Rosetta energy function from correctly identifying native-like models in the absence of experimental data. These benchmarking results confirm that RosettaDEER can effectively leverage sparse experimental data for a wide array of modeling applications built into the Rosetta software suite.

biophysics↗

bioRxiv2018

Talent Identification at the limits of Peer Review: an analysis of the EMBO Postdoctoral Fellowships Selection Process

Scientific peer review is still the most common system for fund allocation despite having been shown in multiple instances to lack accuracy in identifying the most meritorious applications among high quality ones. This study evaluates two aspects of the selection process of the top-ranked applicants to the EMBO Long-Term Fellowship program in 2007. First, the accuracy of the system is evaluated by comparing the level of career progression of the candidates in 2017 with the original award decisions made in 2007. The second aspect, explores the relationship of career progression with indicators derived from the information available to evaluators at the time of application. The results obtained suggest that the peer review system is not substantially better than random selection in identifying the best candidates once an initial pre-selection of the most promising ones is performed. Not only that, the analysis of the indicators studied, some of which have not been analyzed in detail in the past, suggests that among other potential sources of uncertainty, the information available at the time of application is not sufficiently predictive of career progression. As previously described, however, we find clear differences in career progression between men and women. We propose a new mixed model of fellowship evaluation in which peer review is used to select high quality applications, and random allocation of funds is subsequently used to award fellowships among these top ranked candidates.

scientific communication and education↗