Understanding protein dynamics and conformational states is crucial for insights into biological processes and disease mechanisms, which can aid drug development. Recently, several methods have been devised to broaden the conformational predictions made by AlphaFold2 (AF2). We introduce AFsample2, a method using random MSA column masking to reduce co-evolutionary signals, enhancing structural diversity in AF2-generated models. AFsample2 effectively predicts alternative states for various proteins, producing high-quality end states and diverse conformational ensembles. In the OC23 dataset, alternate state models improved (ΔTM>0.05) in 9 out of 23 cases without affecting preferred state generation. Similar results were seen in 16 membrane protein transporters, with 11 out of 16 targets showing improvement. TM-score improvements to experimental end states were substantial, sometimes exceeding 50%, improving from 0.58 to 0.98. Additionally, AFsample2 increased the diversity of intermediate conformations by 70% compared to standard AF2, producing highly confident models potentially representing intermediate states. For four targets, predicted intermediate states were structurally similar to known structural homologs in the PDB, suggesting that they are true intermediate states. These findings indicate that AFsample2 can used to provide structural insights into proteins with multiple states, as well as potential paths between the states.

Background: Survival and drug response are two highly emphasized clinical outcomes in cancer research that directs the prognosis of a cancer patient. Here, we have proposed a late multi omics integrative framework that robustly quantifies survival and drug response for breast cancer patients with a focus on the relative predictive ability of available omics datatypes. Neighborhood component analysis (NCA), a supervised feature selection algorithm selected relevant features from multi-omics datasets retrieved from The Cancer Genome Atlas (TCGA) and Genomics of Drug Sensitivity in Cancer (GDSC) databases. A Neural network framework, fed with NCA selected features, was used to develop survival and drug response prediction models for breast cancer patients. The drug response framework used regression and unsupervised clustering (K-means) to segregate samples into responders and non-responders based on their predicted IC50 values (Z-score). Results: The survival prediction framework was highly effective in categorizing patients into risk subtypes with an accuracy of 94%. Compared to single-omics and early integration approaches, our drug response prediction models performed significantly better and were able to predict IC50 values (Z-score) with a mean square error (MSE) of 1.154 and an overall regression value of 0.92, showing a linear relationship between predicted and actual IC50 values. Conclusion: The proposed omics integration strategy provides an effective way of extracting critical information from diverse omics data types enabling estimation of prognostic indicators. Such integrative models with high predictive power would have a significant impact and utility in precision oncology.

Understanding protein dynamics and conformational states carries profound scientific and practical implications for several areas of research, ranging from a general understanding of biological processes at the molecular level to a detailed understanding of disease mechanisms, which in turn can open up new avenues in drug development. Multiple solutions have been recently developed to widen the conformational landscape of predictions made by Alphafold2 (AF2). Here, we introduce AFsample2, a method employing random MSA column masking to reduce the influence of co-evolutionary signals to enhance the structural diversity of models generated by the AF2 neural network. AFsample2 improves the prediction of alternative states for a broad range of proteins, yielding high-quality end states and diverse conformational ensembles. In the data set of open-closed conformations (OC23), alternate state models improved in 17 out of 23 cases without compromising the generation of the preferred state. Consistent results were observed in 16 membrane protein transporters, with improvements in 12 out of 16 targets. TM-score improvements to experimental end states were substantial, sometimes exceeding 50%, elevating mediocre scores from 0.58 to nearly perfect 0.98. Furthermore, AFsample2 increased the diversity of intermediate conformations by 70% compared to the standard AF2 system, producing highly confident models, that could potentially be on-path between the two states. In addition, we also propose a way of selecting the end-states in generated model ensembles. These solutions could potentially enhance the generation and identification of alternative protein conformations, thereby providing a more comprehensive understanding of protein function and dynamics. Future work will focus on validating the accuracy of these intermediate conformations and exploring their relevance to functional transitions in proteins.

The utility of multi-omics in personalized therapy and cancer survival analysis has been debated and demonstrated extensively in the recent past. Most of the current methods still suffer from data constraints such as high-dimensionality, unexplained interdependence, and subpar integration methods. Here, we propose SurvCNN, an alternative approach to process multi-omics data with robust computer vision architectures, to predict cancer prognosis for Lung Adenocarcinoma patients. Numerical multi-omics data were transformed into their image representations and fed into a Convolutional Neural network with a discrete-time model to predict survival probabilities. The framework also dichotomized patients into risk subgroups based on their survival probabilities over time. SurvCNN was evaluated on multiple performance metrics and outperformed existing methods with a high degree of confidence. Moreover, comprehensive insights into the relative performance of various combinations of omics datasets were probed. Critical biological processes, pathways and cell types identified from downstream processing of differentially expressed genes suggested that the framework could elucidate elements detrimental to a patient’s survival. Such integrative models with high predictive power would have a significant impact and utility in precision oncology.

Accurately capturing the conformational diversity of proteins is essential for understanding their mechanisms and regulation. However, current structure prediction approaches, including AlphaFold derivatives, are largely limited to modeling one dominant conformation. Improved sampling methods have expanded this to predicting two states. Here, we present AFsample3, an enhanced sampling framework built upon AlphaFold3 that substantially improves the generation and selection of diverse protein conformations. Across a benchmark of 238 non-redundant proteins with multiple experimentally determined states, AFsample3 significantly outperforms AlphaFold3 and its predecessor AFsample2 by improved predictions for 28% (67/239) of targets (ΔTM > 0.1) while degrading only 3% (8/239), and increases the number of high-quality alternate-state models (TM > 0.8) by 54% (from 54 to 83; p < 0.0001). AF-sample3 improves alternate-state accuracy for 28% of targets (ΔTM > 0.1) while degrading only 3%, and increases the number of targets with high-quality predictions (TM > 0.8) by over 50% compared to standard AlphaFold3. Ensemble diversity is also markedly enhanced (p < 0.0001), enabling accurate modeling of potential intermediate and or additional states. These results demonstrate that the improved sampling in AFsample3 can capture multiple native-like conformations, representing a significant advance in modeling of protein conformational landscapes.

Proteins are dynamic molecules that transition between conformational states to perform their functions, and characterizing the protein ensemble is important for understanding biology and therapeutic applications. While recent breakthroughs in machine learning have enabled the prediction of high-quality static models of individual proteins, generating reliable estimates of their conformational ensembles remains a challenge. Several recent methods have tried to utilize the evolutionary and structural features captured by effective sequence-to-structure models to enhance conformational diversity in generated models. Most of these approaches involve adapting existing inference pipelines, such as AlphaFold 2, combined with sampling techniques to induce the generation of diverse conformational states. Here, we describe the general problem of predicting structural variations in protein systems, explain the methods designed to address this challenge, explore why they are effective, discuss their limitations, and suggest potential future directions.